Supplementary MaterialsSupplementary Video 1 srep37863-s1. types in the sample. This work shows the utility of an assay purely based on intrinsic biophysical properties of cells to identify changes in HMMR cell state. In addition to a label-free alternative to circulation cytometry in certain applications, this work, also can provide novel intracellular metrics that would not be feasible with labeled methods (i.e. circulation cytometry). Intrinsic physical properties of cells that reflect underlying molecular structure are indicators of cell state associated with a number of processes including malignancy progression, stem cell differentiation, and drug response1,2,3. Nuclear and cytoplasmic structure or morphology have been one of the main tools for histological detection and classification of malignancy. These features include chromatin texture, nuclear shape and cytoplasmic features such as shape and cytoplasmic clearing. Morphology is usually indicative of cell fate, differentiation, and self-renewal capacity. In addition to the expression of certain cell surface markers, cell morphology has been one of the major parameters for validation of pluripotency of human embryonic stem cell (hESC) CGS-15943 and induced pluripotent stem cell (iPSC)4,5,6. Recent studies have recognized morphological properties that distinguish different subpopulations in highly heterogeneous cultures of mesenchymal stem cells7. Morphology-based assays have also been successful in discovery of unique drugs that take action on mammalian cells, filamentous fungi, and yeasts8. Observation of pharmacological classCdependent morphological changes in cells has been considered as a complementary strategy for drug discovery6. Recent work using morphological screening tools have linked morphology to activity of a subset of genes9,10. While morphometric measurements provide information on visible cell structures without external probing, internal and optically transparent architectural features can be probed by measuring cell deformation under an applied stress. Cell mechanical stiffness has recently emerged as an indication of various changes in cells state11 including malignancy cell function, motility, and invasion capacity12,13,14. One study found human metastatic malignancy cells to be more than 70% softer than neighboring benign reactive mesothelial cells1. Embryonic stem cells have also been found to be more deformable than differentiated cells using atomic pressure microscopy and micropipette aspiration15,16. Assaying both external and internal architectural properties of cells through the combinations of morphological and mechanical signatures is expected to provide label-free and low cost biomarkers of cell type or state. Although cell morphological and mechanical characteristics can be indicative of cell state in a variety of cellular processes and conditions, the lack of high-throughput and integrated methods to assay single-cell physical properties, especially from fluid samples, has been a major barrier to adoption of these platforms17. For instance, morphological properties can be measured by automated microscopy, a process that can image tens of cells per second, while cell mechanical properties have CGS-15943 been mainly measured using methods such as atomic pressure microscopy (AFM), optical stretching, or micropipette aspiration, which are single-cell based and manual methods ( 1 cell/sec)1,15,18,19. These methods CGS-15943 do not allow for flow cytometryClike throughputs ( 1,000 cells/sec) and intuitive readouts, which allow sampling of rare subpopulations of cells in a reasonable time period. Emerging methods are now able to measure a few mechanical properties from tens to thousands of cells per second20,21,22, however, these techniques have not yet provided a holistic view of a cell in which multiple internal and visible features of cellular architecture are simultaneously probed. Multiparameter CGS-15943 measurements are important in identifying rare populations of cells, in which additional parameters and sample size provide increased statistical confidence in sub-classification23. In this study, we perform combined mechanical and morphological phenotyping at rates of 1,000 cells/sec.