Conventional data visualization techniques in single-cell analysis (such as two-dimensional dot plots, SPADE, PCA, t-SNE, or UMAP) often fall short in enabling an intuitive understanding of high-parameter flow cytometry data. These methods tend to oversimplify complex biological relationships, lack biologically meaningful interpretations, and offer no principled framework for downstream quantitative analysis. To address these limitations, we present a graph-based (network-based) visualization framework grounded in optimal transport theory. In this framework, cell populations are defined by their marker-expression profiles, and inter-population similarity is quantified using an efficiently computable optimal transport formulation known as the Sinkhorn distance. Our approach produces biologically consistent two-dimensional graph layouts using a phenotype-aware Hamming distance. Structural differences between sample graphs are characterized through a customized graph-edit distance that captures changes in population size, marker expression, and relationships between populations. We demonstrate our methods on two flow cytometry datasets: one from a clinical trial of dendritic cell-based immunotherapy in malignant peritoneal mesothelioma, involving 14 patients sampled at three time points with 14-color panels, and another from FlowCAP-II, which involved 43 acute myeloid leukemia patient samples analyzed with 7-color panels. Our framework produces robust, quantitative visual summaries of cell populations and supports statistical analysis based on graph edit distances, thereby offering new insights into disease progression and treatment response. Ultimately, our method bridges the gap between flow cytometry data visualization and biological interpretation.

Elastic Light Scatter (ELS) profiling is a unique, non-invasive technique for the rapid analysis and identification of bacterial colonies grown on agar. Colonies are inspected using a laser; the resulting light-scatter images have been shown to be species-specific. We examined a probabilistic approach to the analysis of ELS profiles for identification purposes. We analyzed 1701 colonies representing 52 strains across 19 food-related bacterial species representing four phylogenetically distinct families. Each of three ELS-derived feature sets (Patsekin elements, Zernike moments, pseudo-Zernike moments) were included, yielding a total of 2257 features. Each individual feature was binarized using an adaptive threshold set at 50% of its maximum value, a strategy that preserved meaningful differences across feature types of varied scale. A species-level identification matrix was constructed by summarizing positively expressed features across colonies, and representative species profiles were generated for comparison. Several taxa sharing ecological niches and ELS features were combined for improved performance. Use of Hypothetical Median Organism - and Leave-one-out analyses subsequently correctly identified each of 15 defined taxa with high Willcox probability scores. Importantly, test species not included in the database (Arcobacter bilvalviorum, A. faecis, and A. ellisii) were correctly considered as unknown based on a combined analysis of probabilistic scores and taxonomic distances, even when profiles shared partial similarity. A probabilistic approach to identifying bacteria from ELS profiles is an effective means to achieve this goal, and could potentially be used for other methods that generate taxon-specific spectra.

Due to the prevalence of food fraud and its associated risks to human health, food safety and quality assurance systems must become more comprehensive, rapid, and accurate. Food fraud often involves contamination, which refers to the presence of harmful substances in food, including biological, chemical, and physical adulterants. Traditional detection methods, such as microbiological and chemical testing, are limited in sensitivity, are time-consuming, and often lack comprehensive analysis capabilities. This report presents the development of a hybrid Raman and laser-induced breakdown spectroscopy (LIBS) system, referred to as Hy-R-LIBS, for the quantitative analysis of food contaminants on fruit surfaces. In this work, the term hybrid denotes a system in which both spectroscopic modalities are co-housed within a single optical chassis, share a common optical path, and perform automated sequential acquisition on the same sample spot, enabling either independent or joint analysis of the resulting spectra. Raman spectroscopy provides molecular fingerprinting capability, while LIBS enables elemental analysis at low concentrations. The miniaturization of the combined system is feasible because Raman and LIBS share similar optical pathways. To overcome the inherently weak Raman scattering cross-section that limits sensitivity in portable systems, a surface-enhancement strategy based on silver nanoparticles (AgNPs) was employed, which improves both Raman scattering signals and LIBS plasma emission. The study demonstrated that AgNP-assisted surface enhancement improves both LIBS and Raman signals, as confirmed by the quantitative analysis of food contaminants on fruit peel. Pesticide and heavy -metal controls were used, and the limit of detection (LOD) for various contaminants was estimated. The results indicate the feasibility of the hybrid system and the enhancement achieved through surface-enhancement techniques. Overall, the Hy-R-LIBS system offers a promising approach for the quantitative analysis of food contaminants. The combination of these optical spectroscopic techniques enables comprehensive investigations of complex food matrices contaminated with both organic chemicals and metallic residues, providing valuable insights for food safety assurance and public health.

Salmonella typhimurium is responsible for numerous cases of foodborne illnesses, often resulting in severe hospitalizations and fatalities. The imperative to detect and prevent such foodborne pathogens is crucial for safeguarding public health. This research introduces the creation of a quartz-crystal microbalance (QCM) system integrated with a smartphone, aiming to identify the presence of S. typhimurium through a dual-mode approach. This innovative system employs two distinct mechanisms: first, by gauging frequency changes induced by bacterial mass, and second, by quantifying fluorescence intensities arising from bacteria captured by fluorescein isothiocyanate (FITC)-labeled antibodies. The utilization of FITC-labeled antibodies impacts the measurement in two ways: it enhances the frequency-shift signal for mass change and facilitates the visualization of bacterial cells via optical detection. The integration of the smartphone with the QCM system enables the real-time presentation of frequency data and the recording of fluorescence intensities, allowing for the estimation of cell numbers. The smartphone-based system effectively detected S. typhimurium within a concentration range of 10(3)-10(5) CFU/mL following the application of FITC-labeled antibodies.

The application of flow cytometry for sorting and analyzing pathogenic organisms poses significant challenges, not only due to the difficulty of maintaining a sterile environment but, more critically, because of improper handling and disposal of pathogen-containing waste. If viable pathogens remain in the waste stream, they can present serious environmental risks. Conventional practice typically relies on adding chemical disinfectants, which in turn creates complications for subsequent autoclaving. In this work, we demonstrate an alternative approach using UV-C light for bacterial disinfection prior to waste collection. A UV-C device was integrated just before the flow cytometer’s waste chamber, ensuring that liquid waste flowing through the flow cytometry waste tubing was continuously exposed to UV-C irradiation. To validate this approach, we analyzed three bacterial species: Escherichia coli, Salmonella sp., and Klebsiella sp. to determine the efficacy of using such a device in a flow cytometer. Samples from the waste stream were collected both before and after UV-C exposure and plated on agar overnight. Our results showed complete inactivation (100% kill rate) of two bacterial species (E.coli and Klebsiella sp.) and near- complete inactivation of Salmonella sp. following UV-C treatment. This method provides a simple, chemical-free, and effective approach to enhance biosafety and ensure safe waste disposal in laboratories performing flow cytometry with pathogenic organisms.

Conventional data visualization techniques in single-cell analysis (such as two-dimensional dot plots, SPADE, PCA, t-SNE, or UMAP) often fall short in providing an intuitive understanding of high-parameter flow cytometry data. These methods tend to oversimplify complex biological relationships, lack biologically meaningful interpretations, and offer no principled framework for downstream quantitative analysis. To address these limitations, we present a graph-based visualization framework grounded in optimal transport theory. In this framework, cell populations are defined by their marker-expression profiles, and inter-population similarity is quantified using an efficiently computable optimal transport formulation known as the Sinkhorn distance. Our approach produces biologically consistent two-dimensional graph layouts using a phenotypeaware Hamming distance. Structural differences between sample graphs are characterized through a customized graph-edit distance that captures changes in population size, marker expression, and relationships between populations. We demonstrate our methods on two flow cytometry datasets: one from a clinical trial of dendritic cell-based immunotherapy in malignant peritoneal mesothelioma, involving 14 patients sampled at three time points with 14-color panels, and another from FlowCAP-II, which involved 43 acute myeloid leukemia patient samples analyzed with 7-color panels. Our framework produces robust, quantitative visual summaries of cell populations and supports statistical analysis based on graph edit distances, thereby offering new insights into disease progression and treatment response. Ultimately, our method bridges the gap between flow cytometry data visualization and biological interpretation. ### Competing Interest Statement The authors have declared no competing interest. United States Department of Energy, https://ror.org/01bj3aw27, SC-0022260

Despite decades of control efforts, the prevalence of schistosomiasis remains high in many endemic regions, posing significant challenges to global health. One of the key factors contributing to the persistence of the disease is the complex life cycle of the Schistosoma parasite, the causative agent, which involves multiple stages of development and intricate interactions with its mammalian hosts and snails. Among the various stages of the parasite lifecycle, the deposition of eggs and their migration through host tissues is significant, as they initiate the onset of the disease pathology by inducing inflammatory reactions and tissue damage. However, our understanding of the mechanisms underlying Schistosoma egg extravasation remains limited, hindering efforts to develop effective interventions. Microphysiological systems, particularly organ-on-a-chip systems, offer a promising approach to study this phenomenon in a controlled experimental setting because they allow the replication of physiological microenvironments in vitro. This review provides an overview of schistosomiasis, introduces the concept of organ-on-a-chip technology, and discusses its potential applications in the field of schistosomiasis research.

BACKGROUND:Oscillometry may provide the feasible and sensitive tool for objective remote monitoring of paediatric asthma. METHODS:Observational study of school-aged healthy, well-controlled and poorly-controlled asthma performing daily home-based oscillometry for 3-4 months, alongside objective measures of asthma control (Asthma Control Questionnaire weekly and Asthma Control Test monthly), medication use and exacerbations. Day-to-day variability calculated as coefficient of variation (CV) for resistance at 5 Hz (R5), reactance at 5 Hz (X5) and area under reactance curve (AX). Our objective was to examine feasibility, whether day-to-day variability was increased in asthma and correlations with asthma control and exacerbation burden. Clinical exacerbation patterns were examined using principal component analysis and k-means clustering of oscillometry, symptoms, breathing parameters and adherence. RESULTS:Feasibility was 74.9±16.0% in health (n=13, 93.7±16.2 days) and 80.6±12.9% in asthma (n=42, 101.6±24.9 days; 17 well-controlled and 27 poorly-controlled asthma). Increased day-to-day variability in all oscillometry indices occurred in asthma versus health (all p≤0.002), with CV R5 the best discriminator (area under receiver operating characteristics curve 0.88, p<0.001). CV R5 increased during exacerbation and correlated with all asthma control measures and exacerbation burden. Correlations remained when examining non-exacerbation oscillometry data. Two exacerbation patterns were found based on oscillometry data in the pre-exacerbation period, characterised by severity of impairment of R5, X5, AX and CV R5 (n=12, more severe). Findings were similar using post-exacerbation period oscillometry data (n=8, more severe). Symptoms did not differ across exacerbation patterns. CONCLUSIONS:Home-based oscillometry monitoring was highly feasible over extended periods in school-aged asthmatics. Day-to-day oscillometry variability was increased in asthma compared with health, reflected asthma control and exacerbation burden and identified differing exacerbation patterns.

Lateral flow assays (LFAs) are extensively utilized in point-of-care diagnostics due to their affordability, simplicity, and rapid time-to-results. However, their low sensitivity remains a significant limitation, particularly for detecting foodborne pathogens at concentrations below regulatory thresholds. This study evaluated two distinct sensing modalities—photothermal speckle imaging and colorimetric line intensity analysis—for their potential to enhance the sensitivity of commercially available LFAs. Photothermal imaging quantified refractive index shifts induced by plasmonic heating of gold nanoparticles, while colorimetric analysis used smartphone-acquired images processed with machine learning. The photothermal method achieved a limit of detection (LOD) of 2.13 × 105 CFU/mL, while the colorimetric approach, using a logistic regression model with LASSO regularization, achieved an LOD of 105 CFU/mL. While both approaches demonstrated detection thresholds comparable to traditional visual interpretation, the colorimetric method provided an added advantage by enabling quantitative prediction of bacterial concentration through regression modeling. With further optimization of each sensing method, these findings demonstrate the feasibility of improving unmodified commercial LFAs through optical and computational enhancements, offering a promising pathway toward the development of portable biosensing systems for real-time food safety monitoring.

Over the past 6 decades, cytometry has evolved from a niche experimental method into a cornerstone of modern biomedical research. The development of the first cell sorter by Mack Fulwyler laid the groundwork for technologies that now define single-cell analysis. From the early challenges of instrument operation and laser alignment to today's era of automated, high-parameter systems, the field has undergone continual transformation. We now enter what may be termed the "diamond age" of cytometry, an era of exceptional sensitivity, resolution, and analytical depth, driven by innovations such as spectral flow cytometry. Here, we reflect on the historical milestones that shaped the discipline, the cultural and educational shifts within the community, and the future challenges of standardization and quantitative rigor.

Flow cytometry is a versatile analytical technology for measuring physical and molecular characteristics of individual cells or particles in suspension. The technology has had its greatest impact in immunology, enabling the identification and quantification of rare cell populations within complex mixtures, but applications span diverse biological systems including hematopoietic cells, microorganisms, cultured cells, plant cells, gametes, and disaggregated tissues. Target molecules are typically identified using fluorophore-conjugated antibodies, though alternative labeling strategies exist. A key advantage of flow cytometry is the ability to physically isolate cells of interest for downstream applications such as culture, genomic analysis, or functional studies. The field has undergone substantial evolution from conventional filter-based polychromatic systems to spectral cytometry platforms that capture full emission spectra, enabling higher-parameter analyses and more flexible panel design. This review examines current capabilities and limitations of flow cytometry technology, with emphasis on recent advances in spectral detection, quantitative standardization, and computational analysis. We discuss remaining technical challenges and explore emerging opportunities for innovation in excitation systems, detector technology, and integration with artificial intelligence-based analysis platforms. Addressing these challenges will be essential for cytometry to continue driving biological discovery and clinical applications in the coming decades.

Elastic Light Scatter (ELS) profiling is a novel approach for simultaneous detection and identification of bacteria cultured on solid agar media. The profiles comprise a range of different scatter features that can be used jointly or individually as a basis for comparison. We examined the utility of cluster analysis of ELS profiles for classification and identification of bacteria of relevance to foods. A total of 1562 colonies from 48 strains, representing 17 different species distributed among four genera, were examined. Each of three scatter-derived features (Zernike moments, pseudo-Zernike moments, proprietary Patsekin elements) were used individually and in combination for the cluster analysis. Of these, a combination of Patsekin elements and pseudo-Zernike moments yielded clusters that best reflected the known taxonomic relationships among the strains examined. Evidence of Genus-level markers of colony architecture was seen and there was a general agreement of clustering at the species level. Nonetheless, some individual colonies did not cluster with the majority of others from the same taxon, which could reflect an aberrant ELS phenotype, or known challenges in depicting strain relationships using cluster analytical methods. However, when compared with UMAP data processing, relationships between individual colonies were more easily discerned by inspecting the dendrogram. Cluster analysis of ELS profiles is a useful adjunctive tool for the classification and identification of bacteria and results may also be helpful in informing the development and improvement of other data analytical tools for ELS profile analysis.

With the development and expansion of the internet of things, many scientific and engineering instruments are leaving the benchtop restriction and moving on to provide on-site detection. On-site detection requires a complete miniaturization of a benchtop system while maintaining a similar performance with respect to the analyte detection sensitivity. In addition, due to the mobile nature, utilizing a battery source is required. Here we present a portable loop-medicated isothermal amplification detection system for on-site detection and amplification of target analyte via fluorescence detection. The digital twin design incorporates three major components: an isothermal heating chamber, light-tight enclosure for sample insert, and fluorescence imaging system via micro-controllers. The isothermal heating chamber was designed with Peltier heater to provide small form factor accurate temperature control. For light-tight enclosure is a 3D printed device that allows DNA samples to be inserted and fluorescent images to be taken within the chamber. Lastly, fluorescent imaging system operates with a stand-alone camera connected to an Arduino micro-controller. Excitation is provided by blue colored LED and emission is detected via long-pass filter that matches the emission spectrum.

AbstractThe impact of evolving treatment regimens, airway clearance strategies, and antibiotic combinations on the incidence and prevalence of respiratory infection in cystic fibrosis (CF) in children and adolescents remains unclear. The incidence, prevalence, and prescription trends from 2002 to 2019 with 18,339 airway samples were analysed. Staphylococcus aureus [− 3.86% (95% CI − 5.28–2.43)] showed the largest annual decline in incidence, followed by Haemophilus influenzae [− 3.46% (95% CI − 4.95–1.96)] and Pseudomonas aeruginosa [− 2.80%95% CI (− 4.26–1.34)]. Non-tuberculous mycobacteria and Burkholderia cepacia showed a non-significant increase in incidence. A similar pattern of change in prevalence was observed. No change in trend was observed in infants < 2 years of age. The mean age of the first isolation of S. aureus (p < 0.001), P. aeruginosa (p < 0.001), H. influenza (p < 0.001), Serratia marcescens (p = 0.006) and Aspergillus fumigatus (p = 0.02) have increased. Nebulised amikacin (+ 3.09 ± 2.24 prescription/year, p = 0.003) and colistin (+ 1.95 ± 0.3 prescriptions/year, p = 0.032) were increasingly prescribed, while tobramycin (− 8.46 ± 4.7 prescriptions/year, p < 0.001) showed a decrease in prescription. Dornase alfa and hypertonic saline nebulisation prescription increased by 16.74 ± 4.1 prescriptions/year and 24 ± 4.6 prescriptions/year (p < 0.001). There is a shift in CF among respiratory pathogens and prescriptions which reflects the evolution of cystic fibrosis treatment strategies over time.

The proliferation and dissemination of antimicrobial-resistant bacteria is an increasingly global challenge and is attributed mainly to the excessive or improper use of antibiotics. Currently, the gold-standard phenotypic methodology for detecting resistant strains is agar plating, which is a time-consuming process that involves multiple subculturing steps. Genotypic analysis techniques are fast, but they require pure starting samples and cannot differentiate between viable and non-viable organisms. Thus, there is a need to develop a better method to identify and prevent the spread of antimicrobial resistance. This work presents a novel method for detecting and identifying antibiotic-resistant strains by combining a cell sorter for bacterial detection and an elastic-light-scattering method for bacterial classification. The cell sorter was equipped with safety mechanisms for handling pathogenic organisms and enabled precise placement of individual bacteria onto an agar plate. The patterning was performed on an antibiotic-gradient plate, where the growth of colonies in sections with high antibiotic concentrations confirmed the presence of a resistant strain. The antibiotic-gradient plate was also tested with an elastic-light-scattering device where each colony’s unique colony scatter pattern was recorded and classified using machine learning for rapid identification of bacteria. Sorting and patterning bacteria on an antibiotic-gradient plate using a cell sorter reduced the number of subculturing steps and allowed direct qualitative binary detection of resistant strains. Elastic-light-scattering technology is a rapid, label-free, and non-destructive method that permits instantaneous classification of pathogenic strains based on the unique bacterial colony scatter pattern. • Individual bacteria cells are placed on gradient agar plates by a cell sorter • Laser-light scatter patterns are used to recognize antibiotic-resistant organisms • Scatter patterns formed by colonies correspond to AMR-associated phenotypes

Application of flow cytometry to microbiology has been limited due to inadequate availability of bacterial-specific stains, expensive antibody-based fluorophores, ineffective stain cell permeability and challenges in differentiating bacterial cells from cell debris due to their similarity and their small size. In addition, staining cells demands multiple washing steps which limits the sensitivity of detection due to the cell volume that is up to two orders smaller than typical eukaryotic cells. Further, most flow cytometers are not equipped to handle pathogenic organisms. Autofluorescence-based detection of cells can be a useful method for bacterial detection as multiple washing steps can be avoided and it also reduces the time and cost of using stains. Multiple studies have shown that the autofluorescence in bacterial cells are mainly linked to specific proteins, enzymes, or enzyme cofactors such as Flavin Adenine Dinucleotide (FAD) and Nicotinamide Adenine Dinucleotide (NAD) which are involved in bacterial metabolism. In this report, we present a novel method for differentiation between clinically isolated antibiotic resistant and non-resistant bacteria by utilizing their autofluorescence spectral signatures. We utilized a spectral cytometer known as Bigfoot which is equipped with an integrated biosafety cabinet allowing easy handling of pathogenic organisms unlike any other flow cytometers. Bigfoot also has 9 lasers and 54 fluorescence detectors which we utilize to capture the bacterial spectral autofluorescence signatures. As a proof of principle, we initially stressed different types of bacteria ( E . coli and Salmonella sp . ) using gentamicin antibiotics by collecting spectral autofluorescence over different time points. The spectral signatures were compared with the non-stressed bacteria. We observed that the stressed bacteria showed an increase in autofluorescence at distinct excitation (Ultraviolet, Violet, and blue color) and emission wavelengths whereas the non-stressed did not. The same experiments were repeated to compare the autofluorescence signatures between Methicillin-resistant and methicillin susceptible staphylococcus aureus (MRSA and MSSA) which were stressed with oxacillin antibiotics. MSSA showed an increase in autofluorescence between 4 – 6 h after exposure to oxacillin. MRSA on the other hand showed no increase in autofluorescence and the autofluorescence between stressed and non-stressed MRSA had similar signatures. This demonstrated that the antibiotic resistant or susceptible strain can be detected by observing the change in autofluorescence signature at specific wavelengths in a few hours. This label-free, quantitative, and resistant-specific autofluorescence spectral signatures from the Bigfoot spectral flow cytometer could potentially be utilized for rapid detection of antibioticresistant strain.### Competing Interest StatementThe authors have declared no competing interest.

Single photon detection (SPD) is the essential technology for the future of quantum cytometry and quantum biology. We have been developing SPD technology previously reported at DCS2022 but recently achieved detection and recording of photoelectron (PE) pulse width <500ps with 1Gcps saturation count with near 7LOG dynamic range (DR). The current challenge involves developing a spectral photon detection system that works in the range from ultraviolet to near infrared region. We have developed a six decade dynamic range spectrometer from 360nm to 820nm, with a 42 channels fiber array (42CH) that distributes each spectral window onto an individual pixel-coupled silicon photomultiplier (SiPM), each channel has a 10.9nm bandwidth. The detected PE streams of the 42CH are captured with an FPGA at 10Gs/s with 100ps time resolution using multi-GHz electronics and thermoelectric cooling, and produce a huge data stream of 420Gs/s. We have identified interference problems on the system which arise from using conventional packaging with gold wire connection in dry nitrogen such as oscillation, crosstalk between adjacent channels and interference from external radiation such as Wi-Fi and cellular RF signals. To resolve electrical interference and improve signal quality, the sensor chips were mounted on an eight-layer Chip-On-Board (COB). Improving the sensor environment was the other focus for our system. We have designed a two stages thermoelectric device targeted at -30 degrees C with a moisture getter in the sensor package to reduce the thermal electron and the dark count of the SiPM. This design is an innovative approach in the packaging method that helps to control the environment inside the sensor. Earlier photon spectroscopy required a considerable time to scan a full spectral range using a monochromator. Our newly developed 42CH multiwavelength spectrometer allows the capture of a spectral fingerprint in microseconds to microseconds with potential readout in SI units. The system under development will contribute various applications in the fast-developing quantum field.