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Khatib, T. O.

Publications and source records attributed to Khatib, T. O..

2 recordsLinked to original sources

Adaptation of breast cancer cells to serum-free defined medium reveals changes in proliferation, adhesion, and lipid metabolism

Cancer cell metabolic dependencies are strongly influenced by nutrient availability, yet conventional culture conditions contain non-physiological nutrient concentrations and poorly defined serum components that may obscure metabolic vulnerabilities. Here, we investigated the adaptation of three breast cancer cell lines to physiologically relevant, serum-free defined medium (DM). Using a stepwise reduction in fetal bovine serum (FBS), we evaluated proliferation, adhesion, morphology, and transcriptional changes in HER2-positive SK-BR-3, triple-negative MDA-MB-231, and ER-positive/HER2-positive BT-474 cells. The three cell lines differed substantially in their ability to adapt to serum-free conditions. MDA-MB-231 cells adapted readily, maintaining proliferation and adhesion without exogenous substrate coatings. SK-BR-3 cells also achieved sustained proliferation in serum-free DM, but required a serum-derived coating to maintain adhesion. In contrast, BT-474 cells failed to sustain adhesion and proliferation following complete serum withdrawal, although they remained proliferative in medium containing 1% FBS. Adapted cells retained characteristic HER2 or vimentin expression despite changes in morphology and proliferation. RNA sequencing revealed extensive transcriptional remodeling, including reduced expression of cell-cycle-associated programs in SK-BR-3 cells and enrichment of cholesterol homeostasis gene sets in SK-BR-3 and MDA-MB-231 cells. Expression of cholesterol regulatory genes, including SREBF2, HMGCR, and LDLR, increased across all three lines. Together, these findings highlight cell-line-specific adhesion requirements and shared metabolic responses to serum withdrawal, establishing a framework for developing defined culture systems to investigate cancer metabolic dependencies through genetic screening.

cancer biology↗

A live-cell platform to isolate phenotypically defined subpopulations for spatial multi-omic profiling

Numerous techniques have been employed to deconstruct the heterogeneity observed in normal and diseased cellular populations, including single cell RNA sequencing, in situ hybridization, and flow cytometry. While these approaches have revolutionized our understanding of heterogeneity, in isolation they cannot correlate phenotypic information within a physiologically relevant live-cell state, with molecular profiles. This inability to integrate a historical live-cell phenotype, such as invasiveness, cell:cell interactions, and changes in spatial positioning, with multi-omic data, creates a gap in understanding cellular heterogeneity. We sought to address this gap by employing lab technologies to design a detailed protocol, termed Spatiotemporal Genomics and Cellular Analysis (SaGA), for the precise imaging-based selection, isolation, and expansion of phenotypically distinct live-cells. We begin with cells stably expressing a photoconvertible fluorescent protein and employ live cell confocal microscopy to photoconvert a user-defined single cell or set of cells displaying a phenotype of interest. The total population is then extracted from its microenvironment, and the optically highlighted cells are isolated using fluorescence activated cell sorting. SaGA-isolated cells can then be subjected to multi-omics analysis or cellular propagation for in vitro or in vivo studies. This protocol can be applied to a variety of conditions, creating protocol flexibility for user-specific research interests. The SaGA technique can be accomplished in one workday by non-specialists and results in a phenotypically defined cellular subpopulation for integration with multi-omics techniques. We envision this approach providing multi-dimensional datasets exploring the relationship between live-cell phenotype and multi-omic heterogeneity within normal and diseased cellular populations.

cell biology↗