Insight Article

Making Big Data Make Sense: Automating High-Throughput Flow Cytometry

By Tia Byer |
08 November 2021
With an increasing need for large data analysis and validation, this Insight article takes an in-depth look at some of the recent advances in automation technologies affecting the flow cytometry field.

Flow cytometry is a powerful quantitative and preparative tool, allowing for the analytical interrogation of single cells amongst potentially millions of cells in minutes. Since its inception in 1953 and commercial popularisation in the 1970s, flow cytometry has emerged as a leading and sophisticated technology in applying the high-resolution characterisation of single cells within complex cellular networks. In 2020, the global flow cytometry market was estimated at $6.3 billion, and in 2021, it is expected to grow to $6.9 billion. With an annual growth rate of 8.9%, the market, by 2027, is forecast at an impressive $11.5 billion. Key players include Agilent Technologies, Beckman Coulter Inc., NeoGenomics, Bio-rad Laboratories, Cytonome, Sysmex Corporation, Sartorius, and Thermo Fisher Scientific.

Although the tool has a comprehensive and constantly expanding application field, from molecular biology and pathology to immunology, multiparameter flow cytometry generates increasingly complex data sets. The growing trend in complex or ‘big’ data for the industry means novel approaches and technologies are required to address the present need for large-scale data handling. We sat down with leading experts to learn some of the key drivers behind the high-throughput analysis and delves into recent advances in automation for flow cytometry.

Data Management: Why the Need?

Multiparameter flow cytometry allows simultaneous examination of multiple targets on various cell subsets from a relatively limited sample size. It can identify, enumerate, and track cellular markers (both intracellular and intranuclear) during treatment. Whilst the tool enables greater accuracy, increasing the throughput volume and thus complexity inevitably requires better-optimised data management systems. Flow Cytometry Expert at GSK, Dr. Bangwen Xie, sums up the problem, explaining how: “because any errors can be magnified during downstream data analysis of multiparameter flow cytometry, you need quite a robust design and optimisation in place prior to experiment execution”*. He continues by saying, “we need to know more about the procedure for avoiding such pitfalls, which is where improved data quality comes in next for subsequent unbiased data analysis”. Crucial to addressing these issues include streamlining setup details such as panel design and optimisation with relevant control procedures.

Creating a Robust Panel Design:

With large amounts of data comes large amounts of responsibility, and thus, larger margins for errors. According to Dr. Xie, the most pressing need of the flow cytometry biomarker industry comes down to finding out “how to make big data make sense …and how to deliver unbiased data analysis in a cost- and time-effective manner”. Data handling on a large scale presents several challenges that include exponentially increased complexity, reduced accuracy, and compromised consistency. For Nick Jones, Director of Global Flow Cytometry at NeoGenomics, “the analysis gets exponentially harder the more parameters we add”. For example, typically speaking, a panel consisting of 15 colours can have up to 685 different combinations.

Consequently, large data sets in flow cytometry necessitates laborious quality-control procedures. Control measures are nevertheless vital to advancing the technology’s optimisation. Larry A. Sklar, Distinguished Professor Emeritus at The University of New Mexico, states that “when you’re going to be screening several hundreds of thousands of compounds in the early phase, you will have to think about the number of plates that you run every day”. High throughput of data requires a greater number of panels, which will take longer to analyse. Sklar continues, “then if your assay itself is very complex, you might spend a lot more time analysing the data than actually collecting the data”. Implementing a more efficient workflow is, therefore, a necessary and hotly debated strategy moving forward.  

Automation: The Promise for High-Throughput Flow Cytometry

The two main reasons for automation are to make processes run faster and to reduce variability. In particular, Nathan Standifer, Group Director at AstraZeneca, confirms that “in terms of robustness, automation is imperative”. Technologies to optimise accuracy and efficiency enable independent high-throughput processing. Nick Jones identifies the main benefit of automation for flow cytometry as the ability to “upload the set of plates or a set of tubes and being able to walk away to take care of other priorities in the lab”. Computerised and pre-programmed cell work can significantly increase throughput and enables 24/7 operation.

Automation in flow-cytometry is on the rise, especially in the realms of auto-staining. auto-sampling, and auto-analysis. Other trending ML technology applications include control validations such as temperature preservation to maintain sample integrity. Leading companies within this arena include both Sony and Cytek, with the advent of numerous intuitive cell analyser platforms being unleashed between them. By integrating sophisticated robotic automation into the flow cytometry workflow, these inventions ease the burden of instrument operators by enabling optimised large data analysis and validation. 

The Future of Automated High-Throughput Flow Cytometry:

With the continuous advent and development of pioneering automation technologies, the biomarker industry remains at the forefront of the global drug market. However, with every emerging technology comes a unique set of challenges. Within the automated flow cytometry field, there are several key considerations that require addressing. Solutions to large data analysis is not always a fit for purpose workaround. Identifying the right technology and manufacturer will depend upon a company’s specific needs and type of large data sets. Automating simultaneous sampling may also be cost prohibited, requiring significant up-scaling.

So how can we best address these challenges? The market is a constantly evolving field, dominated by ongoing clinical research and development. And with the preference for precision medicine taking centre stage worldwide, there is no sign of it slowing down. As the industry continues to open new vistas in therapeutic testing, we think it is only a matter of time before automated flow cytometry starts taking over as one of the leading biomarker technologies.

* Bangwen Xie is an employee and shareholder of GlaxoSmithKline (GSK). Views and opinions expressed in this article are speaker’s own and do not necessarily represent the position of GSK.

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