How to Identify an Accurate Model for Your Efficacy Study
Recently published in a paper on Nature.com in Scientific Reports, Horizon Discovery have conducted a detailed analysis of CRISPR-Cas9 sensitivity (drop-out) screening to come up with a highly improved and optimised platform. In our analysis, we used a custom ultra-complex sgRNA library and capitalised on Horizon’s streamlined screening pipeline to evaluate fundamental aspects of functional genomic screening, including:
- Side-by-side comparison of the impact of a novel tracrRNA sequence on screen performance
- Direct analysis of the efficacy of two different sgRNA design algorithms
- Evaluation of the effect of cell line ploidy on KO rate and screen quality
- Time-resolved gene drop-out analysis to evaluate the kinetics of CRISPR-Cas9 driven gene knockout
The visualisation of proteins or organelles in cells and other complex biological systems is ‘bread and butter’ stuff for cellular and molecular biologists; it’s performed day-in, day-out in labs across the globe. But this doesn’t mean that the most popular approaches currently used for protein visualisation – dye staining, antibody labelling and fusion protein over-expression systems – are ideal. Almost every scientific technique has its advantages and drawbacks, and the various options currently available for protein visualisation are not exceptions to this rule, so let’s have a look at the pros and cons of each.
Chromosome abnormalities are a characteristic feature of cancer cells. Translocations are large chromosome rearrangements which are key drivers of tumorigenesis and found in various tumour types. Gene fusions result from balanced chromosome translocations and frequently lead to activation and overexpression of an oncogene. The nature of gene fusions strongly correlates with the tumour type, making them very attractive targets for cancer diagnostic or therapeutic intervention.
Chromosomal translocations are triggered in vivo by the simultaneous occurrence of double strand breaks. The scientists at Horizon Discovery have recently published a new robust and precise approach to generating translocations. This advancement facilitates the generation of relevant cell line models for oncology research.
Recycling has always been a smart idea, and nature has its own processes to ensure that waste is kept to a minimum. As Professor Ohsumi discovered, autophagy is the cells way of degrading and recycling cellular components, allowing it to adapt to nutritional deficiency or other environmental influences. Professor Yoshinori Ohsumi, honorary professor and leader of the Cell Biology Unit at the Tokyo Institute of Technology, has been studying autophagy for 27 years. This year's Nobel Laureate discovered and elucidated mechanisms underlying autophagy, according to the Press Release from The Nobel Assembly at Karolinska Institutet.
On Saturday 10th September 2016, a team of intrepid volunteers defied the rain and blustery weather to compete in the Cambridge Dragon Boat Festival 2016 in aid of ACT (Addenbrooke’s Charitable Trust). Proudly representing Horizon Discovery, in their branded t-shirts, were our 11 person team who, unlike several of the more experienced teams, gave an excellent account of themselves in the inclement conditions and managed to stay afloat throughout.
On 5th September at our offices in Cambridge, Lord-Lieutenant, Sir Hugh Duberly KCVO, CBE presented Dr Darrin Disley, CEO of Horizon Discovery, with a Queens Award for Enterprise Promotion.
Darrin was nominated for the Queen's Award for Enterprise Promotion by Linda Allan. Her Majesty The Queen approved the Prime Minister's recommendation that Darrin should receive the Award.
Which Cell line models to use?
Sourcing biological materials that will acurately represent disease biology can be a time consuming and frustrating proceedure. Here we discuss:
- the options available
- and the merits and disavantages of each
- how gene editing technology can provide bespoke cell models of disease
Increasingly the literature suggests that CRISPR's potential for off-target effects is not as “bad” as originally thought. Here are a few things to set your mind at ease if you’re working with the CRISPR-Cas9 system:
Early on in our CRISPR use at Horizon, we learned that that when it comes to guide RNA design not all sgRNAs are created equal. In fact what makes a good guide is still the topic of a great deal of research in the field – and aside of the consensus that a guide with a guanine at position -1 is more likely to have a higher activity, we are still a way from being 100% confident in predicting in silico guide activity in vitro/vivo.