No abstract

Visualizing actin filaments in fixed cells is of great interest for a variety of topics in cell biology such as cell division, cell movement, and cell signaling. We investigated the possibility of replacing phalloidin, the standard reagent for super-resolution imaging of F-actin in fixed cells, with the actin binding peptide ‘lifeact’. We compared the labels for use in single molecule based super-resolution microscopy, where AlexaFluor 647 labeled phalloidin was used in a dSTORM modality and Atto 655 labeled lifeact was used in a single molecule imaging, reversible binding modality. We found that imaging with lifeact had a comparable resolution in reconstructed images and provided several advantages over phalloidin including lower costs, the ability to image multiple regions of interest on a coverslip without degradation, simplified sequential super-resolution imaging, and more continuous labeling of thin filaments.

In single molecule localization-based super-resolution imaging, high labeling density or the desire for greater data collection speed can lead to clusters of overlapping emitter images in the raw super-resolution image data. We describe a Bayesian inference approach to multiple-emitter fitting that uses Reversible Jump Markov Chain Monte Carlo to identify and localize the emitters in dense regions of data. This formalism can take advantage of any prior information, such as emitter intensity and density. The output is both a posterior probability distribution of emitter locations that includes uncertainty in the number of emitters and the background structure, and a set of coordinates and uncertainties from the most probable model.

In single molecule localization-based super-resolution imaging, high labeling density or the desire for greater data collection speed can lead to clusters of overlapping emitter images in the raw super-resolution image data. We describe a Bayesian inference approach to multiple-emitter fitting that uses Reversible Jump Markov Chain Monte Carlo to identify and localize the emitters in dense regions of data. This formalism can take advantage of any prior information, such as emitter intensity and density. The output is both a posterior probability distribution of emitter locations that includes uncertainty in the number of emitters and the background structure, and a set of coordinates and uncertainties from the most probable model.

Tissue microenvironments affect the functional states of cancer cells, but determining these influences in vivo has remained a challenge. We present a quantitative high-resolution imaging assay of single cancer cells in zebrafish xenografts to probe functional adaptation to variable cell-extrinsic cues and molecular interventions. Using cell morphology as a surrogate readout of cell functional states, we examine environmental influences on the morphotype distribution of Ewing Sarcoma, a pediatric cancer associated with the oncogene EWSR1-FLI1 and whose plasticity is thought to determine disease outcome through non-genomic mechanisms. Computer vision analysis reveals systematic shifts in the distribution of 3D morphotypes as a function of cell type and seeding site, as well as tissue-specific cellular organizations that recapitulate those observed in human tumors. Reduced expression of the EWSR1-FLI1 protein product causes a shift to more protrusive cells and decreased tissue specificity of the morphotype distribution. Overall, this work establishes a framework for a statistically robust study of cancer cell plasticity in diverse tissue microenvironments.