Cell size and growth are tightly regulated processes balancing synthesis and metabolism to ensure proper cell function. Deviations from normal growth in response to drug treatment provide insights into the induced cellular dysfunction caused by pharmacotherapy. These changes in biomass growth have shown promise as a marker for drug sensitivity. However, like many biomarkers, the output cannot be treated as binary. Just as cancer cells are heterogenous on the molecular level, individual cells’ biomass response to drug treatment can range from cell death to no effect. It is therefore important to begin to survey for the full range of biomass growth responses to drug treatment to understand the dysfunction induced. Here, we explore the response of different cancer cell lines to treatments that induce biomass growth changes ranging from apoptosis to senescence to simply delayed regrowth. These longer-term studies, ranging up to six days of constant monitoring, aid in the interpretation of more commonly performed shorter-term biomass growth experiments and identify cells of interest for further molecular characterization in these cell lines.
The robustness of Quantitative Phase Imaging (QPI) has enabled QPI to be used in applications to answer both research and clinical questions. QPI requires no labels, is non-destructive, and has nanoscale sensitivity to 3-d morphology. Various applications have included recording cellular force dynamics, identifying parasite-infected red blood cells, detecting cancer prognosis from colon cancer samples, and most recently predicting therapeutic sensitivity from live cell biomass accumulation measurements in patient derived xenograft (PDX) mice. However, challenges remain for clinical adoption, as QPI-based methods must first be proven more effective than current standards of care and patient inconvenience and costs must be minimized. Here we applied basic upgrades to previously described High Speed Live Cell Interferometry (HSLCI) to predict in vivo and in vitro PDX mouse tumor sensitivity to a range of cancer drugs from only Fine Needle Biopsy of the tumor. As demonstrated by our group and many others, the applications of QPI are not limited to the clinical realm. Using HSLCI, we revealed the growth dynamics of senescent and control H460 lung large cell carcinoma cells treated with cancer chemotherapy. The continued improvements in optics and throughput of QPI promise to answer many more clinical and basic science questions.
Standard algorithms for phase unwrapping often fail for interferometric quantitative phase imaging (QPI) of biological samples due to the variable morphology of these samples and the requirement to image at low light intensities to avoid phototoxicity. We describe a new algorithm combining random walk-based image segmentation with linear discriminant analysis (LDA)-based feature detection, using assumptions about the morphology of biological samples to account for phase ambiguities when standard methods have failed. We present three versions of our method: first, a method for LDA image segmentation based on a manually compiled training dataset; second, a method using a random walker (RW) algorithm informed by the assumed properties of a biological phase image; and third, an algorithm which combines LDA-based edge detection with an efficient RW algorithm. We show that the combination of LDA plus the RW algorithm gives the best overall performance with little speed penalty compared to LDA alone, and that this algorithm can be further optimized using a genetic algorithm to yield superior performance for phase unwrapping of QPI data from biological samples.
This paper describes how multiple interferometric techniques, implemented on a single system, can be combined to provide viable measurements of both biosensors and cells, enabling collection of data in environments and with timescales not previously achievable.
Here we report application of imaging interferometry to the study of nanomechanical motion in biosensors and living biological systems. Using strobed interferometric microscopy we are able to probe the dynamic behavior of individual (100 x 500 x 1 micron) cantilevers in an eight cantilever array over frequencies from 0 - 1 MHz. In a related approach, we have developed an interferometric method to measure cell-specific mechanical signals in real time. This yields real-time diagnostic information about cell structure, metabolism and movement, along with response to chemical and physical stimuli. Our new approach makes use of "nanomirrors" fixed to the cell membrane. These mirrors act as nanoscopic displacement probes and can be interrogated, rapidly, by optical profiling metrology.
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