Electrofusion of single cells in picoliter droplets

We present a microfluidic chip that enables electrofusion of cells in microdroplets, with exchange of nuclear components. It is shown, to our knowledge for the first time, electrofusion of two HL60 cells, inside a microdroplet. This is the crucial intermediate step for controlled hybridoma formation where a B cell is electrofused with a myeloma cell. We use a microfluidic device consisting of a microchannel structure in PDMS bonded to a glass substrate through which droplets with two differently stained HL60 cells are transported. An array of six recessed platinum electrode pairs is used for electrofusion. When applying six voltage pulses of 2-3 V, the membrane electrical field is about 1 MV/cm for 1 ms. This results in electrofusion of these cells with a fusion yield of around 5%. The operation with individual cell pairs, the appreciable efficiency and the potential to operate in high-throughput (up to 500 cells sec-1) makes the microdroplet fusion technology a promising platform for cell electrofusion, which has the potential to compete with the conventional methods. Besides, this platform is not restricted to cell fusion but is also applicable to various other cell-based assays such as single cell analysis and differentiation assays

Predicting outcomes in radiation oncology-multifactorial decision support systems

With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome-including survival, recurrence patterns and toxicity-in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner