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Dive Into In-vivo Single Cell Transcript Analyses during Genomics Research Europe - An interview with Philip Day ...

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Are you ready for a sunny break from the office routine and a refreshing dive into the latest in genomics research? Then head to Barcelona, Spain for Genomics Research Europe from 16-17 October. Even better, the conference is comprised of two tracks that will deliver in-depth examinations of: New Applications in PCR & Next-Generation Sequencing, and Epigenetics, microRNAs and Non-coding RNAs in Disease. Manchester University Reader Philip Day will be presenting a paper on In-vivo Single Cell Transcript Analyses for Systems Modelling. During his research, single cell measurements of DNA, mRNA and protein for bcr-abl was measured in K562 cells to produce a steady state model of BCR.ABL protein abundance per mRNA molecule. His presentation will focus on his findings concerning the impact of heterogeneity and development of in-vivo transcript measurements. In today's blog, he shares some of the insights he will cover in more depth during his speech. 

Q: What do you feel are the current challenges in in-vivo single cell transcript analyses (with reference to your work if possible)? 

A: The reason for engaging this type of work is to improve the resolution of data generated to enable enhanced detailed analysis of life processes. For me this means to build more detailed mathematical disease models and using the cell as a common denominator better facilitates the integration of different 'omics since all results relate to a specific molecular event, or perturbation that has taken place. The deal here of course is to be able to isolate analytes from single cells without affecting the accuracy of analysis of other classes of 'omics markers. Challenges are manifold and mainly technical, and will require the determination of cellular heterogeneity to allow this 'background' to be distinguished from signal.

Q: Can you tell us a bit about the innovative tools that you have developed for precise and accurate measurement of transcripts in heterogeneous tissues and for application in cancer studies?

A: My group has expertise to carefully measure transcripts in populations of single cells employing qPCR for high sensitivity. Our knowhow more relates to how cells are handled prior to qPCR, and we have employed FACS and microfluidic platforms to manipulate cells to enable populations of a few hundreds of cells to be subjected to qPCR analysis. To access cells in an intact form, we have developed cancer studies based in leukemia as these cells are already single cells and therefore complex tissue disaggregation can be avoided, and details of tissue architecture do not require annotation as blood has no structure. Studies have developed means to co-analyse mRNA with exo and endo metabolites without any losses of these analytes. We have also more recently been working on the use of atomic force microscopy to add and remove nucleic acids across cells to enable exact perturbation experiments to be performed, where exact removal or addition of molecules of nucleic acids can be correlated to a measured response.

Q: What are some of the more interesting findings that have come about as a result of your investigations into the systems pathology of neuroblastoma and leukemia, and molecular biomarkers relating to prognostics? 

A: We have measured heterogeneity across populations of K562 cells. We have also characterised that sub-populations of cells exist that differ in bcr-abl expression (mRNA and protein) and that high levels correlate to more aggressive cell growth and resistance to therapy with imatinib. The term 'biomarker' is another way of saying that we do not know what the molecule does, and this term we avoid! We are trying to ascribe molecular candidates functional roles through the formation of mathematical models and initial studies have been published. miRNAs are being analysed for their impact on changing regulation of bcr-abl and potential repressor proteins are being determined. 

Q: How effective do you find working through close collaborative studies with groups in analytical instrumentation, miniaturization, data analysis, text mining and systems biology? What additional insights does such collaboration provide? 

A: This is quite a challenge, but pretty much all very positive! The field specialists are diverse and all are keenly interested to contribute translationally to the improvement of cancer treatment. The main point is that none of these can be treated separately and they need themselves to be integrated to enable process and platforms to be built that will enable meaningful models of disease (leukemia) to be constructed. Each is an academic discipline in its own right and all are co-evolving. This and similar studies are hopefully helping to channel the developments in these fields to permit more streamlined and compatible operating systems and software to be developed. 

Q: In your experience, how has in vivo single cell transcript analyses changed over the last 5-10 years and what impact has it had on your research? 

A: To my understanding in vivo single cell transcript analysis is very limited. More usually GFP-protein in vivo analyses are performed which is not quite the same. Therefore the field has not impacted greatly on my research, and this is the reason why I have sought colleagues who have knowhow to permit transcript analysis in vivo. 

Q: What are some of the future innovations you think will occur in the area of biomarker integration for enabling personalized healthcare? 

A: Personalized healthcare will only be realized following innovations in single cell analyses. The question links into the earlier question that concerns the integration of highly collaborative studies. If we believe that molecular regulation has a part to play in the control of organ and organism function, then this must be evident at the level of the single cell. Perhaps the field may prove to be highly dependent upon the development of holistic systems models of life and disease processes, such as those relating to the virtual human, but this is a long way from reality. Never the less, colonizing database resources with reliable quantitative molecular data is key to understanding risk groups. 

You can find out more details about the program and register to attend here.