Session 4 – Technological Development / Emerging Technologies

Friday, 12.09.2025, 10:45 am – 12:00 pm

Chairs: Toralf Kaiser, Michael Kirschbaum

In the session on “Technical Development / Emerging Technologies,” innovative approaches to addressing cytometric challenges will be presented. This includes exceptional analytical techniques in sorting processes, as well as advancements in device development and technical improvements of cytometric instruments. Experts will cover the latest developments in the analysis of extracellular vesicles and showcase microfluidic techniques that enhance the precision and efficiency of cytometric analyses. We will learn about solutions for analyzing or sorting particles of extraordinary sizes, properties, or handling requirements. In summary, novel technical strategies will be highlighted that overcome existing limitations and open up new possibilities for research, therapy and diagnostics.

Szandor Simmons

Institute of Physiology, Charité – Universitätsmedizin Berlin, Germany

Liquid Biopsies and Beyond: Cytometric Profiling of Extracellular Vesicles for Diagnostic and Therapeutic Translation

Extracellular vesicles (EVs), including exosomes, microvesicles or apoptotic bodies, are nanoscale lipid bilayer delimited mediators of intercellular and interorgan communication with increasing importance as biomarkers and potential effectors in a variety of diseases. Their molecular cargo, including proteins, sugars, lipids, and  nucleic acids, reflects the state of their cells of origin, providing minimally invasive insight into physiological and pathological processes. EVs are increasingly understood not only as passive biomarkers, but also as active mediators of disease through the horizontal transfer of bioactive molecules in all types of body fluids.

Flow cytometry has emerged as a central platform for high-throughput, single-particle analysis of EVs. However, flow cytometry has limitations when applied to small EVs (<200 nm) due to fundamental physical and technical constraints: limited light-scattering sensitivity, the low refractive index of EVs, coincidence (swarm) detection at high particle concentrations, weak fluorescence signals and background noise. In addition, the lack of EV-specific calibration standards affects reproducibility and interlaboratory comparability. As a result, conventional FACS systems – designed for cell-sized particles – struggle to reliably detect and characterise EVs. However, recent advances in nanoscale and spectral flow cytometry, especially when combined with real-time imaging, are now enabling multi-parametric, high-resolution profiling of EVs and expanding our understanding of EV functionality and tissue targeting.

I aim to stimulate discussion on how to implement robust and reproducible EV cytometry workflows suitable for both basic and translational research, with the ultimate goal of integrating EV profiling into clinical diagnostics and precision medicine.

Biosketch

Szandor Simmons heads the Junior Research Group “Immunodynamics” at the Institute of Physiology, Charité – Universitätsmedizin Berlin. His research focuses on inflammatory aspects of pulmonary and cardiovascular pathophysiology, in particular the role of molecular mediators and immune system components in diseases such as pulmonary hypertension, atherosclerosis and acute respiratory distress syndrome. A key aspect of his work is the investigation of extracellular vesicles (EVs) as mediators of immune communication and inflammation in cardiovascular disease and pulmonary inflammation, exploring their potential as biomarkers and therapeutic targets.

Vinod Devaraj

V. Devaraj1, D. Kage1, A. Wolf1, K. Heinrich1, H.-D. Chang1,2, T. Kaiser1

  1. German Rheumatology Research Center (DRFZ) – Flow Cytometry Core Facility
  2. Technical University Berlin – Institute for Biotechnology/Department of Cytometry

Flow cytometry for bacteria and microbial communities using advanced scattered light detection and specialized data analysis

Flow cytometry is a well-established technology for a wide range of applications in the biomedical and environmental sciences. State-of-the-art flow cytometry heavily relies on fluorescent staining of certain cell subsets while emerging technologies for label-free cell analysis focus on imaging or imaging-related methods. For the analysis of bacterial samples, both approaches face limitations. On the one hand, only a small number of specific reagents for fluorescent labeling of bacteria are available, restricting the obtainable amount of information about a sample. On the other hand, imaging, especially at high throughput, does not provide sufficient resolution to detect subtle morphological differences between bacteria types or their ambient conditions.

In order to circumvent these limitations, we have developed a custom-built flow cytometer with sorting capabilities. It uses the signal pulse shapes of scattered light detected in a multitude of different angles during the particle transit through the laser beam. Such pulse shapes with angular resolution carry morphological information beyond the capabilities of conventional forward and side scattered light detection. The extended detection of scattered light is combined with deep neural networks for detailed analysis and classification of light scattering signatures. This technology allows to identify bacteria species and subsets from different growth states or other conditions.

Future work will address the development of high-speed sorting capabilities using artificial intelligence for sort decisions. Moreover, the label-free analysis of microbial samples opens up possibilities in fast and straightforward diagnostics and is potentially useful for antibiotics resistance testing.

Martin Hussels

Martin Hussels, Alexander Putz, Jonas Gienger
Physikalisch-Technische Bundesanstalt (PTB), Abbestr. 2-12, 10587 Berlin, Germany

Novel waiting-time based method for coincidence correction in cell counting

The issue of coincident counting of multiple cells simultaneously has been a long-standing challenge in the field of hematology, with the potential to impact the accuracy of measurement results. In particular, in the context of reference measurement methods for cell counting in quality assurance in hematology, coincidence counting represents a significant source of uncertainty in the measurement process. In the 1960s to mid 1970s, several papers were published discussing methods for correcting coincidence counting in Coulter counters (e.g. [1]). Although accurate cell counting is crucial for widely utilized parameters such as the complete blood count, there has been a paucity of publications on coincidence correction thus far. However, numerous patents have been filed by cytometer manufacturers describing a variety of coincidence correction methods. All of these methods are based on quantities that are accumulated either during or after the measurement process. They rely on instrument-specific parameters, which limits their applicability to different instruments. There are also standards for reference measurement methods for erythrocyte and thrombocyte counting that describe coincidence correction through dilution series experiments. These are not instrument specific but require excessive preparation and measurements to achieve coincidence-corrected counts.

We present a novel patent pending method based on statistical analysis of cell event times, enabling flexible analysis and coincidence correction. The correction is currently conducted post-measurement; however, integration into device software may also be feasible. Furthermore, an analysis of single cell types in complex systems can be conducted, and the method does not rely on instrument-specific parameters. We present the results of erythrocyte count and compare the performance of the method to the reference measurement method listed at the Joint Committee for Traceability in Laboratory Medicine (JCTLM).

[1] Wales, and Wilson, Rev. Sci. Instrum. 32(10), 1132–1136 (1961).

Stefanie Spiegler

Stefanie Spiegler1,2, Bob Fregin1,2, Doreen Biedenweg1, Lea Graichen1, Antonia Bähr1, Una Janke3, Mihaela Delcea2,3, Oliver Otto1,2,*
1 Zentrum für Innovationskompetenz: Humorale Immunreaktionen bei kardiovaskulären Erkrankungen, University of Greifswald, Greifswald, Germany
2 Deutsches Zentrum für Herz-Kreislauf-Forschung e.V., partner site Greifswald, Greifswald, Germany
3 Institute of Biochemistry, University of Greifswald, Greifswald, Germany
* corresponding author: oliver.otto@uni-greifswald.de

Label-free identification of microplastic particles in living human cells

The tremendous application of plastic material in all fields of life causes its ubiquitous distribution in the environment and by that a constant exposition of all living systems to macro-, micro- and nano-sized plastics, which enter organisms via ingestion, inhalation or absorption. While several studies investigated the occurrence and distribution of macroplastics in e.g. fish or seabirds, little is known about the amount of plastic inside single cells from biomaterials. To establish a method to identify micro- and nanoplastics inside living cells, we used real-time deformability cytometry (RT-DC) to characterize human endothelial (HUVEC) and macrophage (THP-1) cells after exposure to fluorescently-labelled spherical plastic particles of different sizes (50 nm, 200 nm, and 1000 nm) and with or without surface modifications. First, we identified all except 50 nm bare polystyrene particles to be efficiently taken up by both cell types. Carboxylation and amination of particles increased uptake rates almost everywhere. By dividing the datasets into plastic-positive and -negative cells, we statistically analyzed biophysical properties between groups. Although the majority of cells was densely packed with plastic particles, just a trend towards lower cell deformation and higher elasticity could be observed lacking statistical significance. Given the fact, that particles appear as dark spots in the brightfield images of cells, we focused on parameters based on variations in pixel intensity. For particles exceeding 500 nm in size our data convincingly show that the mean cellular brightness and its standard deviation (similar to granularity) are reliable parameters to detect microplastic inside cells. These results have been confirmed by confocal microscopy. In summary, we demonstrate the application of RT-DC as a label-free high-throughput assay to identify microplastics inside individual cells from large populations within minutes.