Flow cytometry has evolved at an extraordinary pace over the last decade. What was once considered a complex panel of eight or ten colors has given way to experiments capable of analyzing more than forty parameters simultaneously. The rise of spectral flow cytometry, increasingly large sample cohorts, and the emergence of artificial intelligence (AI)-powered analytical tools are fundamentally changing how researchers interpret cytometry data.
Against this backdrop, an important question is being raised by researchers and laboratory managers alike: Does it still make sense to spend hours performing manual gating, or is it time to embrace automated data analysis?
The answer, as with most technological advances, is not simply yes or no. Manual gating remains an extremely valuable technique, but the scientific landscape has changed dramatically, and today’s research demands are very different from those of just a decade ago.
Since the introduction of the first commercial flow cytometers, manual gating has been the cornerstone of flow cytometry data analysis. Every experienced cytometrist is familiar with the workflow: removing debris using FSC and SSC, excluding doublets, selecting viable cells, and identifying cell populations based on specific markers.
For decades, this approach has remained the gold standard because it provides something that no algorithm can fully replicate: complete control over every analytical decision.
Researchers are not simply observing data—they are interpreting it based on biological knowledge and experimental context. This expertise makes it possible to recognize artifacts, identify unusual events, and adapt the analysis whenever samples behave differently than expected.
For these reasons, manual gating continues to be widely used in both clinical diagnostics and research laboratories. Its longevity is not due to tradition alone, but because it still offers an exceptional level of confidence and interpretability.
The real challenge lies in how dramatically flow cytometry itself has evolved.
Ten years ago, panels containing eight to ten markers were considered advanced. Today, many laboratories routinely work with panels of thirty, forty, or even fifty parameters. Combined with increasingly large patient cohorts, multicenter studies, and millions of events acquired per sample, the amount of information generated has grown exponentially.
In this environment, relying exclusively on manual gating is becoming increasingly inefficient. The issue is not that manual gating is inaccurate; rather, the volume and complexity of modern datasets have surpassed what traditional workflows were designed to handle. As the number of analytical decisions increases, so does operator variability.
Even highly experienced researchers analyzing the same FCS file may draw slightly different gates or define population boundaries differently. Neither analysis is necessarily wrong, but small subjective differences can significantly affect reproducibility when hundreds or thousands of samples are involved.
One of the most significant developments in recent years has been the emergence of high-dimensional flow cytometry.
The goal is no longer limited to identifying well-characterized immune populations. Researchers increasingly seek to discover novel cell subsets and previously unrecognized biomarkers.
This is where manual gating begins to reveal its limitations. Traditional gating follows a sequential strategy based on prior biological knowledge. In other words, researchers search for populations they already know exist. Unsupervised computational algorithms work very differently.
Rather than focusing on predefined populations, they analyze every relationship within the dataset and automatically group cells according to similarities across dozens of parameters. This subtle difference completely changes the scientific approach.
While manual gating answers existing biological questions, computational methods are capable of generating entirely new hypotheses.
Artificial intelligence has become one of the most discussed topics in biomedical research, but its role in flow cytometry is often misunderstood.
Current AI-based tools do not replace researchers or independently decide which cell populations are biologically relevant.
Instead, they process enormous volumes of multidimensional data using sophisticated mathematical models capable of identifying patterns that would be virtually impossible to detect manually.
Algorithms such as FlowSOM, UMAP, t-SNE, PhenoGraph, and a growing number of machine learning models allow researchers to visualize highly complex cellular relationships.
For example, these tools may identify rare immune cell subsets associated with responses to immunotherapy or detect subtle phenotypic changes between patient groups that would remain unnoticed through conventional gating.
Rather than replacing scientific expertise, artificial intelligence dramatically expands researchers’ analytical capabilities.
Current evidence suggests the answer is no.
Manual gating is unlikely to disappear anytime soon. What is changing is the idea that complex datasets can—or should—be analyzed exclusively through manual gating. Leading laboratories are increasingly adopting hybrid workflows.
Researchers still perform essential quality control procedures, remove artifacts, exclude dead cells and doublets, and establish biologically meaningful initial gates. Automated algorithms then explore the remaining multidimensional data to identify hidden cellular patterns or unexpected populations.
Finally, researchers return to interpret these findings within their biological context.
In other words, artificial intelligence is not replacing human expertise—it is enhancing it.
Another major driver behind automated analysis is the growing emphasis on scientific reproducibility.
Funding agencies, regulatory bodies, and scientific journals increasingly require analytical workflows that produce consistent and reproducible results.
Manual gating inevitably introduces some degree of subjectivity. Even the same analyst may define slightly different gates when repeating an analysis weeks later.
Computational algorithms, however, apply identical analytical criteria every time they are executed. This does not necessarily make them universally superior, but it does provide an important level of standardization, particularly in large multicenter studies and biomarker discovery projects.
Fields such as immunotherapy, hematological malignancies, cell therapy, and precision medicine stand to benefit enormously from these improvements.
Over the coming years, artificial intelligence will become an increasingly common component of flow cytometry workflows.
However, success will not come from abandoning manual gating. Instead, it will depend on integrating the strengths of both approaches.
Experienced researchers will remain essential for designing antibody panels, validating biological findings, interpreting results, and recognizing experimental artifacts. At the same time, computational tools will dramatically reduce analysis time, improve reproducibility, and reveal cellular populations that might otherwise remain undetected.
The future of flow cytometry is therefore not a competition between scientists and algorithms. It belongs to laboratories capable of combining the strengths of both.
Manual gating will absolutely remain relevant in 2026, but it can no longer be viewed as the sole strategy for analyzing increasingly complex flow cytometry datasets.
The rapid adoption of spectral flow cytometry, high-dimensional analysis, and artificial intelligence is reshaping the field. Rather than replacing manual gating, these technologies complement it, allowing researchers to extract far more biological information from every experiment.
Ultimately, the future of flow cytometry lies not in choosing between manual gating and automated analysis, but in combining both approaches to generate more reliable, reproducible, and biologically meaningful results.