A discussion on single-photon sensors and Cytometry 3.0

In this interview, we delve into advancements and innovations in flow cytometry with Greg Gmyrek, a Senior Scientist at Miftek Corporation. With a focus on integrating single-photon sensors into cellular analysis, Greg shares insights into his role, the innovative solutions he has developed, and the challenges he has faced in pushing the boundaries of this technology. From the advantages of single-photon sensors to the evolving landscape of flow cytometry, this conversation highlights the exciting future of cellular system analysis and the tools driving progress in the field.

1. Could you explain a bit about your role at Miftek Corporation. What excites you about integrating single-photon sensors and how do you apply this to flow cytometry?

As a Senior Scientist, my responsibilities include designing and implementing new assay development and validation processes for instruments utilizing single-photon sensors. This technology is experiencing significant growth. As a result, the 21^st century will be recognized as the quantum era because of broad application of single-photon sensors and the development of secure quantum communication, computing and remote sensing. However, the most promising applications of single-photon sensors are within the biological sciences. These sensors are particularly valuable in scenarios where detecting extremely low molecule quantities, absolute photon counts, or fluorescence lifetimes is critical. This includes single-molecule and digital assays, lifetime-based measurements, and rare-event detection in flow cytometry.

The potential of this technology lies in expanding our understanding of biological systems. For example, lifetime-resolved autofluorescence measurements of NADH and FAD via photon counting provide insights into oxidative versus glycolytic metabolism, which can be applied in reproductive biology (such as sperm cell studies), cancer research and biomanufacturing. Single-photon sensors can also be used to detect rare events, such as biomarker-positive cells occurring at frequencies below 0.01%, where photon-counting statistics enhance specificity beyond traditional analog detection methods.

Additionally, these sensors facilitate drug screening with lifetime readouts, enabling the monitoring of drug-induced changes in nuclear structure and metabolism in certain cancer models, capturing shifts that conventional flow cytometry may overlook. Differences in lifetime measurements of DNA dyes and chromatin-binding probes can also provide more precise information regarding cell cycle stages and chromatin condensation. Moreover, single-photon sensors could be advantageous for the absolute quantification of extracellular vesicles.

In summary, single-photon sensors are particularly suitable for assays that (a) require digital, single-molecule counting, or (b) leverage fluorescence lifetime and photon-arrival data as additional informative parameters beyond conventional intensity measurements in flow cytometry.

2. Could you share some of the innovative solutions you’ve been working on for analyzing cellular systems and their functionalities?

Let’s take a step back and consider what a photon is; it is a fundamental particle of light within the visible wavelength that can be viewed as the smallest unit of energy corresponding to a binary “bit.” Traditional flow cytometers measure photocurrent output, which is a continuous, analog signal proportional to the number of photons detected. In contrast, single-photon measurement (photon counting) produces a discrete pulse for each photon detected.
In the realm of single-photon fluorescence detection, this approach allows for precise quantification of each photon based on the International Standard Units (SI), specifically the joule. Our motivation and focus in developing single-photon detection technology are aimed at transforming current flow cytometry into a fully digital and quantitative instrument. We believe that similar to the advancements seen with spectral flow cytometry, incorporating single-photon sensors into flow cytometry paves the way for a new era—which we refer to as Cytometry 3.0 or quantum cytometry.

3. What gives single-photon sensors an edge over other technologies in flow cytometry?

There are several reasons why single-photon sensors are superior detectors compared to those used in modern flow cytometers. As previously noted, single-photon sensors excel at converting each detected photon into precise digital and timing information, enabling single-molecule sensitivity, reliable lifetime measurements, and truly quantitative flow cytometry. These detectors register each photon with a digital pulse, allowing direct analysis of photon statistics rather than relying solely on analog signals. One advantage of our technology at Miftek is that each sensor has the unique ability to produce photon counts (totally digital) simultaneously with analog data – so it collects both digital and analog.

Additionally, users can count photons from individual biomarkers and relate these counts to their quantum yield, facilitating the development of absolute concentration scales for biomarkers instead of relying solely on relative measures such as mean fluorescence intensity (MFI). Moreover, single-photon detectors are compact, which supports the creation of smaller, potentially portable instruments. They interface naturally with field-programmable gate arrays (FPGAs) and digital time-tagging systems, simplifying accurate counting and complex gating schemes compared to traditional analog instruments. In summary, single-photon sensors, thanks to their per-photon and time-resolved capabilities, enable advanced assay types. These include single-molecule detection, quantum-verified rare-event detection, and high-speed lifetime flow—that conventional detectors cannot fully support or can only approximate.

4. What are some of the biggest challenges you’ve come across in your role? Is there anything particularly difficult about working with single-photon sensors?

The primary limitations are related to non-ideal detector physics, primarily concerning dead time and dark noise, as well as system-level challenges such as complex electronic throughput. Dead time is associated with photon flux; following each avalanche event, most single-photon detectors are temporarily inactive for tens to hundreds of nanoseconds. At high photon flux levels, this response compression restricts achievable count rates and impacts linearity. However, we developed our own detector which is extremely fast, and we call it dead-timeless as it does not have the long dead time of commercial detectors. Dark noise is an inherent characteristic of single-photon operation and causes sensitivity to thermally generated photons and background, but this is reduced by cooling the sensors. Additionally, single-photon counting requires high-speed time-tagging, typically involving FPGA-based time-taggers with sub-nanosecond resolution capable of sustaining tens of millions of counts per second per channel without pile-up, to effectively utilize lifetime or photon-correlation information. Even with ideal detectors, fundamental limitations imposed by Poisson photon statistics and background signals persist.

5. What’s your process for overcoming these challenges?

In my role, we develop and test processes on a micro-scale prior to scaling them up to the final instrument. Initially, we designed our own photon sensor, which can be integrated into our built-in laser-induced photon spectroscopy (LIPS) and time-correlated multi-photon counting systems. These systems provide high sensitivity and a broad dynamic range. With this equipment, we developed an adaptive threshold method to improve count linearity and reduce saturation in photon detection. This, along with patented signal processing enables us to distinguish overlapping photon signals. Our observations also indicate that cooling the device reduces the dark count rate and that statistical or machine learning-based discrimination—based on timing, waveform or correlation patterns—effectively filters out dark counts, thereby cleaning the data even with less-than-ideal hardware.

As previously mentioned, a photon can be considered analogous to a bit, meaning that key information is contained in the time domain. This necessitates significant advancements in electronics to surpass the capabilities of current flow cytometers. At Miftek, we collaboratively worked on developing nearly every component of our instrument, utilizing in-house developed tools and solutions, as well as cutting-edge advancements in electronics and optics, to build a fully digital and quantitative flow cytometry system.

6. How do you utilize different software platforms and data visualization tools to analyze flow cytometry data effectively? In your opinion, which tools and platforms do you find most useful?

The brief answer is that you maximize the benefits of flow data by utilizing different tools for various stages of the workflow (or integrating them into a single platform), including quality control and gating, analysis of high-dimensional structure, and final statistics and figures. The selection of data analysis tools largely depends on the type of analysis you plan to perform and your budget. FlowJo and FCS Express are fundamental platforms that support both basic and advanced data analysis through either built-in plugins or the ability to develop custom data analysis workflows. They are relatively affordable in terms of annual subscriptions and are widely used across many laboratories and flow cytometry core facilities, as many users are familiar with them and they cover the majority of functions needed.

Other options include software packages such as CytoSpec and PlateAnalyzer, developed by researchers at Purdue University (IN, USA). These tools offer functionalities similar to FlowJo and FCS Express but are entirely free of charge. For more complex flow cytometry datasets, researchers often turn to cloud-based software solutions such as Cytobank, OMIQ, Cytolution or CellEngine. Among them OMIQ is well-designed and highly integrated with other tools, including EasyPanel (for staining panel design), GraphPad Prism (for statistical analysis) and Luma (an AI-driven platform for data integration and management). Compared to competitors like Cytobank and Cytolution, OMIQ is relatively cost-effective. Currently, it offers over 30 fully integrated algorithms covering dimensionality reduction, clustering, statistical and differential analysis, trajectory inference, data cleaning, normalization and batch correction.

A common challenge with cloud-based analysis platforms—designed as self-service workspaces where users can customize workflows for topics ranging from basic immunophenotyping to high-dimensional exploratory analysis—is that data analysis generally requires some experience and training. Therefore, some users may prefer platforms specifically tailored for automated population discovery. In such cases, Ozette or TerraFlow might be better options to consider.

7. How do you see the field evolving? What innovations do you forsee?

Future innovations in flow cytometry will focus on increasing the amount of information obtained per cell (more parameters, functional readouts), enhancing standardization and automation, and expanding beyond traditional large core facilities. Since the launch of the first flow cytometry analyzer in the late 1960s (ICP‑11 developed by Wolfgang Göhde and commercialized by ParTec (Munich, Germany)), polychromatic flow cytometry has reached its technical capability. It can analyze approximately 28–29 colors, and achieving this capability took over 40 years. In contrast, the development of spectral flow cytometry has enabled the successful analysis of 50-color panels within less than 10 years of the technology’s commercial introduction. Consequently, spectral flow cytometry is experiencing rapid growth, with eight vendors currently manufacturing spectral flow instruments.

Will polychromatic cytometry instrumentation remain relevant?

Yes, because it will take time for spectral cytometry to gain wider adoption in clinical laboratories. I anticipate that regulatory approvals will facilitate broader use in diagnostics and immune monitoring. Ongoing advancements in detector technology, optics and dye chemistry will further enhance sensitivity and enable the creation of increasingly complex staining panels on spectral flow instruments.

Notably, many pharmaceutical companies, such as GSK (London, UK), have started leveraging spectral flow cytometry for large dataset analysis. In the coming years, I expect to see the development of fully integrated, automated flow cytometry systems for sample acquisition. They will be expanded to include automated data analysis (similar to GateNet tools). Integration of artificial intelligence (AI) into instruments and cloud-based software could promote standardization across different sites and reduce operator dependence. Supervised AI-driven analysis tools are likely to become commonplace, similar to emerging solutions like Hema.to and other machine learning approaches that prioritize timing and accuracy for clinical decision-making.

This progression may lead to a shrinking workforce in flow cytometry core facilities, CROs, and pharmaceutical companies using flow cytometry instrumentation. The traditional barriers to entry into the field will diminish significantly compared to 20–30 years ago, making the skillset required for laboratory work much more accessible.
I anticipate ongoing advancements in label-free flow cytometry, where imaging readouts combined with machine learning will enable functional, high-content, and often sort-capable cell analysis without the use of fluorochromes. Among the benefits of this technology are reduced reagent costs, simplified workflows and the ability to perform repeated or in-process measurements. I believe that further development of AI-based analysis pipelines will eventually advance label-free cytometry from proof-of-concept devices to regulated clinical and manufacturing settings.

I also anticipate ongoing advancements and development in flow cytometry applications within veterinary immunology for dogs and cats. Currently, technology is significantly underutilized relative to its potential in comparison to human medicine. As species-specific reagents improve and high-parameter panels are developed, flow cytometry is expected to become a routine, high-utility tool for diagnosing, monitoring, and personalizing treatments for dogs and cats. Its adoption in dogs is expected to progress more rapidly, given that many canine-specific antibodies are available and validated panels already exist. In cats, the use of flow cytometry is increasing but remains limited due to a smaller availability of species-specific antibodies.

Both polychromatic and spectral flow cytometry instruments remain semi-quantitative tools. I believe that within a timeframe of 5–10 years, these will ultimately be replaced by fully digital instruments equipped with new generations of detectors. The question is whether these new instruments will become more portable, tailored to specific applications, or whether they will evolve into complex, all-in-one systems offering more features but potentially limiting accessibility. The answer depends on the availability, development and validation of applications designed for these instruments. In collaboration with users, we will determine their needs to address specific scientific questions and prioritize the technological advancements that should be implemented first.

About the author:

Grzegorz (Greg) Gmyrek
Senior Scientist
Miftek Corporation (IN, USA)

Grzegorz (Greg) Gmyrek is a Senior Scientist working at Miftek Corporation. He is a member of the development team focused on the intersection of systems biology and the design of innovative instrumentation utilizing single-photon sensors, with the goal of addressing challenges in cellular systems and ultimately providing solutions for research and diagnostic applications. Prior to this position, Greg worked as a Scientist for 10+ years in multiple laboratories, including Purdue University Cytometry Laboratory (IN, USA), Washington University School of Medicine (MO, USA), Northwestern University Feinberg School of Medicine (IL, USA), Oklahoma Medical Research Foundation, and The University of Oklahoma Health Sciences Center (both OK, USA). He enjoys working across diverse research areas within cellular and molecular biology and immunology systems. His long-term interests focus on various aspects of cellular and molecular immunology, particularly in the regulation of innate and adaptive immunity, with the goal of identifying translational applications and treatment strategies for infectious diseases, autoimmunity, cancer, aging and gynecology. Additionally, he has a strong interest in instrumentation that can be applied to advanced immunological research, contributing to personalized medicine and diagnostics. Specifically, he has developed expertise in flow cytometry, earning certification as a specialist from the American Society for Clinical Investigation and operator credentials from the European Society for Clinical Cell Analyses. Greg earned his MS and PhD in Biological Sciences in Poland, and he has been working in the United States since 2008.

The opinions expressed in this interview are those of the interviewee and do not necessarily reflect the views of Bioanalysis Zone or Taylor & Francis Group.