New screening technology will make immunologists’ jobs a lot easier. Source: Lorenzo Cafaro via Stocksnap

Every T-cell immunologist has suffered through the tediousness of screening for antigen-specific CD8+ T-cell clones and the disappointment of a negative screen. Thankfully, a new automated T-cell screening technique published in the journal Integrative Biology will ease the hands-on burden and intractable technical difficulty of screening.1 By automating the process of screening, team members can spend more time analyzing screening data rather than going through the motions of setting up screens. In order to collaboratively analyze the vast amounts of data generated by the new automated process, a collaborative informatics suite is necessary to fully utilize the opportunity opened by new technology.

Manual Screening Renders Few Hits

Traditional screening for high avidity CD8+s is a multi-step process which requires irradiative induction of mutations followed by an assay testing the irradiated cells in isolation against the target antigen after a lengthy outgrowth process.2 The mutants can then be functionally assessed via flow cytometry or other ELISA to determine which are considered to be high avidity. 3 Each step of the screening process is labor intensive, produces little data, and requires considerable technical skill.

Common methods for CD8 screening also require luck. In order to guarantee that the epitope mutants to be screened are uniform rather than a mix of cells with different epitopes, a number of challenges must be overcome:

  • Single cell cultures must be made probabilistically by halving the number of cells in each subculture until there is a 50% chance of a cell inhabiting the culture
  • The single cell cultures must be grown out to a quantity of cells that can be manipulated experimentally
  • The screen for high avidity must not deplete the entire colony of cells grown out from each single cell culture
  • Screen hits must be grown out further and analyzed by other team members
  • The screen hits must eventually be used in other experiments further down the pipeline

Presently, screens are conducted in relatively small batches, and little effort is made to differentiate mutants within the screen until they have been confirmed as having high avidity. Mathematics dictate that at least half of all time spent initially preparing mutant subcultures will be wasted on cultures that do not actually have cells. In the brutal numbers game of screening, only the highest avidity mutants are catalogued and analyzed. Given that scope of screening is critical, future screening endeavours would be massively benefitted by robust collaboration between research groups so that hits and techniques could be shared.

Following the initial bottleneck of single cells, too many cells, or no cells at all, the mutants must be grown out to a population that is large enough to manipulate experimentally in the screen itself. Given that outgrowth must begin from a single cell and mutants are notoriously finicky with their growth patterns, this process takes weeks. After outgrowth, the clones are actually screened for avidity, which takes a couple of days. The current methods for CD8 screening are inherently costly and produce very little data relative to the amount of hands-on time required.

Immunologists are familiar with the margins of error inherent with excessive precision pipetting, and estimate far lower success rates from the original culture preparation. In the present paradigm of CD8 avidity screening, the designation of most effective clonal colony is limited by the amount of time and resources researchers are willing to invest performing screens rather than the positive identification of an “ideal” screen hit. Given that different research groups have different definitions of screening hits, the field is ripe for disruption by a new technique which would enable researchers to choose between a variety of screening hits.

A common strategy is to delegate one member of the team to screening full time whereas other members follow up on anything that is above the hit threshold within the screen. The collaborative scaffolding required for traditional CD8 screening is minimal, and also tends to relegate team members to the chore of screening. The boredom caused by endless screening is a well-known joke in the biotech industry, yet the danger of burnout caused by repetition is real. Much of this danger could be alleviated by increased data sharing and collaboration between research groups which are screening against the same targets. Automation of the screening process is also a much-pursued avenue for decreasing the personnel resources required for screening.

Automated Screening is a Norm in the Making

The prospect of automating antigen specific CD8 screening has tantalized immunologists. The previously mentioned microraft technique pioneered by the group at Chapel Hill in North Carolina is an especially promising attack on traditional screening methods. Using an automated fluidic system paired with a traditional cell culture automation suite, researchers were able to drastically reduce the hands-on time required to perform screens. Given that small-scale automation can be readily scaled, this research-grade solution will soon be replicated and used to produce vast amounts of data where before there existed only sparse pieces. Rather than wishing for single hits, researchers will be forced to keep track of numerous positive antigen-specific clonal populations which have differing levels of avidity.  

Increasing the bandwidth of data exiting the screening process poses challenges of its own, however. While immunologists are currently accustomed to handling perhaps half a dozen of potential clones to screen at present, increasing the capacity and throughput of each screen will be commensurate with more data. As the hands on time commitment drops, immunologists will be able to build gradients of avidity if their data management is adequate. If data management continues at the community’s current standard, much of the power and efficiency of the new screening methods will go to waste.

More Data Means More Collaboration

The danger of continuing with data analysis and collaboration as usual is not readily apparent. The new screening technology could easily churn through clones at a higher rate and produce many positive hits for satisfied immunologists, while still not living up to its full potential. Much of the new method’s value comes in data which would be traditionally discarded because of the rarity of positive hits. In collaborative groups, only the highest avidity clones are discussed.

A powerful collaboration suite could improve the process of sharing screening hits in a few ways:

  • Aid division of labor for confirmation of positive hits
  • Offer several thresholds for which screening hits constitute positive hits depending on an individual’s projects
  • Accept input from outside collaborators who may have similar data but different goals for the screen
  • Prevent duplication of work with groups pursuing same project and open up opportunities to initiate collaboration if a suitable positive hit can be used for more than one group’s project

With multiple positive hits to choose from, dividing labor between members of the team will be necessary, and the team will no longer have to bear the chore of screening. Analyzing data and assigning responsibility for following up on screening hits will take on increasing importance, as will a more mature view of how to handle screening data. Rather than a boolean approach to items within a screen, researchers will be able to identify structural differences within receptors that correlate to different avidities without going through the difficulty of in-depth biochemical simulations.

In order to take advantage of the new screening techniques for CD8s, several new tasks must be supported by an informatics and data based collaboration suite for immunologists:

  • Group analysis of patterns within screening hits
  • Investigation into screening hit receptor biochemistry
  • Comparison of screening hit properties
  • Construction of sensible avidity gradients among positive hits
  • Passing screening hit data forward in the research pipeline

With BIOVIA’s informatics suite Designed to Cure, research conducted using the new automated screening techniques will be understandable as a forest of data trees which begin at a positive screening hit rather than a single individual tree with branching experiments. As such, the combination of Designed to Cure and automated screening promises to disrupt the practice of CD8 immunology and revolutionize conceptions of antigen specificity and avidity. Contact us today to find out how BIOVIA can help you get the most out of the latest technology.

  1. “Identification and isolation of antigen-specific cytotoxic T lymphocytes with an automated microraft sorting system.” Dec 5, 2016, https://www.ncbi.nlm.nih.gov/pubmed/27853786
  2. “Functional TCR Retrieval from Single Antigen-Specific Human T Cells Reveals Multiple Novel Epitopes.” December 2014, http://cancerimmunolres.aacrjournals.org/content/2/12/1230
  3. “Comparison of immunofluorescence and enzyme-linked immunoabsorbent assay and immunoglobulin G avidity techniques for screening of anti: Toxoplasma antibodies among single serum sample pregnant women in Tabriz, Iran.” Jan-Mar 2015, https://www.ncbi.nlm.nih.gov/pubmed/25673590