RESUMEN
The experimentally accessible degree of undercooling of single micron-sized liquid pure tin drops has been studied via differential fast scanning calorimetry. The cooling rates employed ranged from 100 to 14,000 K/s. The diameter of the investigated tin drops varied in the range from 7 to 40 µm. The influence of the drop shape on the solidification process could be eliminated due to the nearly spherical shape of the single drop upon heating and cooling and the resultant geometric stability. As a result it became possible to study the effect of both drop size and cooling rate in rapid solidification experimentally. A theoretical description of the experimental results is given by assuming the existence of two different heterogeneous nucleation mechanisms leading to crystal nucleation of the single tin drop. In agreement with the experiment these mechanisms yield a shelf-like dependence of crystal nucleation on undercooling. A dependence of crystal nucleation on the size of the tin drop was observed and is discussed in terms of the mentioned theoretical model, which can possibly also describe the nucleation for other related rapid solidification processes.
RESUMEN
Multiple myeloma (MM) is characterized by significant genetic diversity at subclonal levels that have a defining role in the heterogeneity of tumor progression, clinical aggressiveness and drug sensitivity. Although genome profiling studies have demonstrated heterogeneity in subclonal architecture that may ultimately lead to relapse, a gene expression-based prediction program that can identify, distinguish and quantify drug response in sub-populations within a bulk population of myeloma cells is lacking. In this study, we performed targeted transcriptome analysis on 528 pre-treatment single cells from 11 myeloma cell lines and 418 single cells from 8 drug-naïve MM patients, followed by intensive bioinformatics and statistical analysis for prediction of proteasome inhibitor sensitivity in individual cells. Using our previously reported drug response gene expression profile signature at the single-cell level, we developed an R Statistical analysis package available at https://github.com/bvnlabSCATTome, SCATTome (single-cell analysis of targeted transcriptome), that restructures the data obtained from Fluidigm single-cell quantitative real-time-PCR analysis run, filters missing data, performs scaling of filtered data, builds classification models and predicts drug response of individual cells based on targeted transcriptome using an assortment of machine learning methods. Application of SCATT should contribute to clinically relevant analysis of intratumor heterogeneity, and better inform drug choices based on subclonal cellular responses.