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Abstract The recurrence of cancer following chemotherapy treatment is a major cause of death across solid and hematologic cancers. In B-cell acute lymphoblastic leukemia (B-ALL), relapse after initial chemotherapy treatment leads to poor patient outcomes. Here we test the hypothesis that chemotherapy-treated versus control B-ALL cells can be characterized based on cellular physical phenotypes. To quantify physical phenotypes of chemotherapy-treated leukemia cells, we use cells derived from B-ALL patients that are treated for 7 days with a standard multidrug chemotherapy regimen of vincristine, dexamethasone, and L-asparaginase (VDL). We conduct physical phenotyping of VDL-treated versus control cells by tracking the sequential deformations of single cells as they flow through a series of micron-scale constrictions in a microfluidic device; we call this method Quantitative Cyclical Deformability Cytometry. Using automated image analysis, we extract time-dependent features of deforming cells including cell size and transit time (TT) with single-cell resolution. Our findings show that VDL-treated B-ALL cells have faster TTs and transit velocity than control cells, indicating that VDL-treated cells are more deformable. We then test how effectively physical phenotypes can predict the presence of VDL-treated cells in mixed populations of VDL-treated and control cells using machine learning approaches. We find that TT measurements across a series of sequential constrictions can enhance the classification accuracy of VDL-treated cells in mixed populations using a variety of classifiers. Our findings suggest the predictive power of cell physical phenotyping as a complementary prognostic tool to detect the presence of cells that survive chemotherapy treatment. Ultimately such complementary physical phenotyping approaches could guide treatment strategies and therapeutic interventions. Insight box Cancer cells that survive chemotherapy treatment are major contributors to patient relapse, but the ability to predict recurrence remains a challenge. Here we investigate the physical properties of leukemia cells that survive treatment with chemotherapy drugs by deforming individual cells through a series of micron-scale constrictions in a microfluidic channel. Our findings reveal that leukemia cells that survive chemotherapy treatment are more deformable than control cells. We further show that machine learning algorithms applied to physical phenotyping data can predict the presence of cells that survive chemotherapy treatment in a mixed population. Such an integrated approach using physical phenotyping and machine learning could be valuable to guide patient treatments. 

Abstract The role of transcription factors and biomolecules in cell type conversion has been widely studied. Yet, it remains unclear whether and how intracellular mechanotransduction through focal adhesions (FAs) and the cytoskeleton regulates the epigenetic state and cell reprogramming. Here, it is shown that cytoskeletal structures and the mechanical properties of cells are modulated during the early phase of induced neuronal (iN) reprogramming, with an increase in actin cytoskeleton assembly induced by Ascl1 transgene. The reduction of actin cytoskeletal tension or cell adhesion at the early phase of reprogramming suppresses the expression of mesenchymal genes, promotes a more open chromatin structure, and significantly enhances the efficiency of iN conversion. Specifically, reduction of intracellular tension or cell adhesion not only modulates global epigenetic marks, but also decreases DNA methylation and heterochromatin marks and increases euchromatin marks at the promoter of neuronal genes, thus enhancing the accessibility for gene activation. Finally, micro‐ and nano‐topographic surfaces that reduce cell adhesions enhance iN reprogramming. These novel findings suggest that the actin cytoskeleton and FAs play an important role in epigenetic regulation for cell fate determination, which may lead to novel engineering approaches for cell reprogramming.