 The paper that we wish to address with this video is identifying analytics for high-throughput bioprocessor development studies. The impetus for the paper really comes from the industrial need to be able to move quickly in a high-throughput mode through bioprocessor development studies and to avoid the analytical methods they deploy from becoming a bottleneck to those high-throughput applications. We then set out to understand what it is about particular analytical methods that makes them appropriate or not appropriate for deployment in a high-throughput mode. I'm now going to hand over to the first of the course of Simi Kong and Spiritus Consonides to talk about the way in which we've addressed this problem. Our investigation starts by looking into features of analytical methods that can affect the high-throughput nature. In our paper we have defined the high-throughput nature as a function of time and simple amount of requirements. For this purpose, we have defined features relating to these two types of requirements as seen on the figure. After defining these features, we have tabulated and discussed them in the paper for a selection of 70 analytical methods from the perspective of a map purification process. These methods are divided into six categories such as spectroscopic, PCR, immuno-assays, electrophoretic, chromatographic, and their tests for analyte such as endotoxins, lipids, total proteins, nucleic acids, maps and the variants, HCPs and Leach Protein A. All this information is available digitally in the company and supporting information. The link is shown here. Having defined and tabulated features of analytical methods, we then focus on how to employ these in order to judge the suitability of analytics to high-throughput applications. This made apparent that the time-related features of analytics are best considered collectively instead of on a standalone basis. This is because the total time requirements posed by an analytical method as a function of the number of samples it analyzes may not follow a single linear trend, as opposed to the total sample amount of requirements. This revealed to us an additional factor affecting the high-throughput nature of analytical methods, and this is the InVision Experimental Study itself. We therefore proposed that the assessment of the high-throughput nature of analytics should consider all features of analytics in a collective fashion, and at the same time issued also taking into account the general characteristics of planned experimental work. This would give valuable insights on the criticality of the contribution of the different features of analytical methods towards the high-throughput nature. Carrying out this assessment in parallel to the planning of experimental work, we therefore allowed to identify analytics that provide the best fit to purpose and hence, should facilitate the completion of the study. So in conclusion, SIME has taken us through how the nature of the analytical methods can be defined in terms of their capacity to be used in a high-throughput mode of operation. We then demonstrated how those features have been tabulated in an easily accessible format, and that is to enable readers and people accessing this article to be able to apply the same methodology to other analytical techniques. And then SPIROS has identified how we can use those parameters and metrics to identify those particular analytical methods that can be operated in a high-throughput mode, taking into account the particular features that make them suitable for that mode of operation. The work reported in this paper has been carried out by SPIROS Consonides as an engineering doctorate, sponsored by GE Healthcare and by SIME.com, a post-doctoral researcher in the EPSLC Centre for Innovative Manufacturing of Immersion to Macromolecular Therapies.