In the event of a large-scale incident involving radiological or nuclear exposures, there is a potential for large numbers of individuals to have received doses of radiation sufficient to cause adverse health effects. It is imperative to quickly identify these individuals in order to provide information to the medical community to assist in making decisions about their treatment. The cytokinesis-block micronucleus assay is a well-established method for performing biodosimetry. This assay has previously been adapted to imaging flow cytometry and has been validated as a high-throughput option for providing dose estimates in the range of 0-10 Gy. The goal of this study was to test the ability to further optimize the assay by reducing the time of culture to 48 h from 68 h as well as reducing the volume of blood required for the analysis to 200 μL from 2 mL. These modifications would provide efficiencies in time and ease of processing impacting the ability to manage large numbers of samples and provide dose estimates in a timely manner. Results demonstrated that either the blood volume or the culture time could be reduced while maintaining dose estimates with sufficient accuracy for triage analysis. Reducing both the blood volume and culture time, however, resulted in poor dose estimates. In conclusion, depending on the needs of the scenario, either culture time or the blood volume could be reduced to improve the efficiency of analysis for mass casualty scenarios.

Imaging flow cytometry, which combines the advantages of flow cytometry and microscopy, has emerged as a powerful tool for cell analysis in various biomedical fields such as cancer detection. In this study, we develop multiplex imaging flow cytometry (mIFC) by employing a spatial wavelength division multiplexing technique. Our mIFC can simultaneously obtain brightfield and multi-color fluorescence images of individual cells in flow, which are excited by a metal halide lamp and measured by a single detector. Statistical analysis results of multiplex imaging experiments with resolution test lens, magnification test lens, and fluorescent microspheres validate the operation of the mIFC with good imaging channel consistency and micron-scale differentiation capabilities. A deep learning method is designed for multiplex image processing that consists of three deep learning networks (U-net, very deep super resolution, and visual geometry group 19). It is demonstrated that the cluster of differentiation 24 (CD24) imaging channel is more sensitive than the brightfield, nucleus, or cancer antigen 125 (CA125) imaging channel in classifying the three types of ovarian cell lines (IOSE80 normal cell, A2780, and OVCAR3 cancer cells). An average accuracy rate of 97.1% is achieved for the classification of these three types of cells by deep learning analysis when all four imaging channels are considered. Our single-detector mIFC is promising for the development of future imaging flow cytometers and for the automatic single-cell analysis with deep learning in various biomedical fields.

Blood-derived extracellular vesicles (EVs) hold great therapeutic potential. As blood contains mixed EV populations, it is challenging to study EVs originating from different cells separately. Blood cell concentrates manufactured in blood banks offer an excellent non-invasive source of blood cell-specific EV populations. To study blood cell-specific EVs, we isolated EVs from platelet (TREVs) and red blood cell (EryEVs) concentrates and characterized them using nanoparticle tracking analysis, imaging flow cytometry, electron microscopy and western blot analysis and co-cultured them with peripheral blood mononuclear cells (PBMCs). Our aim was to use imaging flow cytometry to investigate EV interaction with PBMCs as well as study their effects on T-lymphocyte populations to better understand their possible biological functions. As a conclusion, TREVs interacted with PBMCs more than EryEVs. Distinctively, TREVs were uptaken into CD11c+ monocytes rapidly and into CD19+ B-lymphocytes in 24 h. EryEVs were not uptaken into CD11c+ monocytes before the 24-h time point, and they were only seen on the surface of lymphocytes. Neither TREVs nor EryEV were uptaken into CD3+ T-lymphocytes and no effect on T-cell populations was detected. We have previously seen similar differences in targeting PC-3 cancer cells. Further studies are needed to address the functional properties of blood cell concentrate-derived EVs. This study demonstrates that imaging flow cytometry can be used to study the distinctive differences in the interaction and uptake of EVs. Considering our current and previous results, EVs present a new valuable component for the future development of blood-derived therapeutics.

Acute myeloid leukemia (AML) is a heterogenous blood cancer with a dismal prognosis. It emanates from leukemic stem cells (LSCs) arising from the genetic transformation of hematopoietic stem cells (HSCs). LSCs hold prognostic value, but their molecular and immunophenotypic heterogeneity poses challenges: there is no single marker for identifying all LSCs across AML samples. We hypothesized that imaging flow cytometry (IFC) paired with artificial intelligence-driven image analysis could visually distinguish LSCs from HSCs based solely on morphology. Initially, a seven-color IFC panel was employed to immunophenotypically identify LSCs and HSCs in bone marrow samples from five AML patients and ten healthy donors, respectively. Next, we developed convolutional neural network (CNN) models for HSC-LSC discrimination using brightfield (BF), side scatter (SSC), and DNA images. Classification using only BF images achieved 86.96% accuracy, indicating significant morphological differences. Accuracy increased to 93.42% when combining BF with DNA images, highlighting differences in nuclear morphology, although DNA images alone were inadequate for accurate HSC-LSC discrimination. Model development using SSC images revealed minor granularity differences. Performance metrics varied substantially between AML patients, indicating considerable morphologic variations among LSCs. Overall, we demonstrate proof-of-concept results for accurate CNN-based HSC-LSC differentiation, instigating the development of a novel technique within AML monitoring.

Optofluidic time-stretch imaging flow cytometry (OTS-IFC) provides a suitable solution for high-precision cell analysis and high-sensitivity detection of rare cells due to its high-throughput and continuous image acquisition. However, transferring and storing continuous big data streams remains a challenge. In this study, we designed a high-speed streaming storage strategy to store OTS-IFC data in real-time, overcoming the imbalance between the fast generation speed in the data acquisition and processing subsystem and the comparatively slower storage speed in the transmission and storage subsystem. This strategy, utilizing an asynchronous buffer structure built on the producer-consumer model, optimizes memory usage for enhanced data throughput and stability. We evaluated the storage performance of the high-speed streaming storage strategy in ultra-large-scale blood cell imaging on a common commercial device. The experimental results show that it can provide a continuous data throughput of up to 5891 MB/s.

Currently, various functionalized nanocarrier systems are extensively studied for targeted delivery of drugs, peptides, and nucleic acids. Joining the approaches of genetic and chemical engineering may produce novel carriers for precise targeting different cellular proteins, which is important for both therapy and diagnosis of various pathologies. Here we present the novel nanocontainers based on vectorized genetically encoded Myxococcus xanthus (Mx) encapsulin, confining a fluorescent photoactivatable mCherry (PAmCherry) protein. The shells of such encapsulins were modified using chemical conjugation of human transferrin (Tf) prelabeled with a fluorescein-6 (FAM) maleimide acting as a vector. We demonstrate that the vectorized encapsulin specifically binds to transferrin receptors (TfRs) on the membranes of mesenchymal stromal/stem cells (MSCs) followed by internalization into cells. Two spectrally separated fluorescent signals from Tf-FAM and PAmCherry are clearly distinguishable and co-localized. It is shown that Tf-tagged Mx encapsulins are internalized by MSCs much more efficiently than by fibroblasts. It has been also found that unlabeled Tf effectively competes with the conjugated Mx-Tf-FAM formulations. That indicates the conjugate internalization into cells by Tf-TfR endocytosis pathway. The developed nanoplatform can be used as an alternative to conventional nanocarriers for targeted delivery of, e.g., genetic material to MSCs.

Cryopreservation of sperm is an essential technique in assisted reproduction in cattle. The objective of the study was to systematically review and synthesize the literature on bull semen quality evaluation based on the comparison of morphological and metabolic parameters of cryopreserved bovine spermatozoa such as DNA integrity, mitochondrial status, plasma membrane alterations, total motility, and morphology (% of abnormal cells). The electronic databases PubMed, Web of Sciences, Scopus, and Google Scholar were searched up to December 2023. Studies and references were included if they reported the following parameters: DNA integrity, mitochondrial status, plasma membrane alterations, total motility, and morphological aberrations (% of abnormal cells) for conventional cryopreserved bovine spermatozoa. After an electronic search, out of 1,526 original studies, only 40 were included in the meta-analysis. Standardized mean differences (SMD) with 95% confidence intervals were estimated for the chosen studies, and a meta-analysis was performed using a random effects model. The tau-squared (tau2) and inconsistency index (I2) quantified heterogeneity among different studies. The regression analysis for the evaluated parameters showed a positive correlation between mitochondrial membrane potential (MMP), total motility, and abnormal morphology and a negative correlation between DNA fragmentation index (DFI) and total motility and MMP. Moreover, subgroup analysis demonstrated similar associations for dairy and non-dairy bull breeds, albeit with lower I2 values. The presence of publication bias was confirmed by Egger\'s test, except for the MMP parameter. A multi-parametric analysis of morphological and metabolic parameters can address the existing limitations of cryopreserved bovine spermatozoa quality assessment. Combining imaging flow cytometry (IFC) with standardization of sperm pre-processing and optimization of the experimental protocols may help to differentiate sperm from cellular debris and cytoplasmic droplets of similar size and alleviate limitations demonstrated by conventional sperm analysis.

Imaging flow cytometry (ImFC) represents a significant technological advancement in the field of cytometry, effectively merging the high-throughput capabilities of flow analysis with the detailed imaging characteristics of microscopy. In our comprehensive review, we adopt a historical perspective to chart the development of ImFC, highlighting its origins and current state of the art and forecasting potential future advancements. The genesis of ImFC stemmed from merging the hydraulic system of a flow cytometer with advanced camera technology. This synergistic coupling facilitates the morphological analysis of cell populations at a high-throughput scale, effectively evolving the landscape of cytometry. Nevertheless, ImFC\'s implementation has encountered hurdles, particularly in developing software capable of managing its sophisticated data acquisition and analysis needs. The scale and complexity of the data generated by ImFC necessitate the creation of novel analytical tools that can effectively manage and interpret these data, thus allowing us to unlock the full potential of ImFC. Notably, artificial intelligence (AI) algorithms have begun to be applied to ImFC, offering promise for enhancing its analytical capabilities. The adaptability and learning capacity of AI may prove to be essential in knowledge mining from the high-dimensional data produced by ImFC, potentially enabling more accurate analyses. Looking forward, we project that ImFC may become an indispensable tool, not only in research laboratories, but also in clinical settings. Given the unique combination of high-throughput cytometry and detailed imaging offered by ImFC, we foresee a critical role for this technology in the next generation of scientific research and diagnostics. As such, we encourage both current and future scientists to consider the integration of ImFC as an addition to their research toolkit and clinical diagnostic routine.

Myelodysplastic syndrome is primarily characterized by dysplasia in the bone marrow (BM), presenting a challenge in consistent morphology interpretation. Accurate diagnosis through traditional slide-based analysis is difficult, necessitating a standardized objective technique. Over the past two decades, imaging flow cytometry (IFC) has proven effective in combining image-based morphometric analyses with high-parameter phenotyping. We have previously demonstrated the effectiveness of combining IFC with a feature-based machine learning algorithm to accurately identify and quantify rare binucleated erythroblasts (BNEs) in dyserythropoietic BM cells. However, a feature-based workflow poses challenges requiring software-specific expertise. Here we employ a Convolutional Neural Network (CNN) algorithm for BNE identification and differentiation from doublets and cells with irregular nuclear morphology in IFC data. We demonstrate that this simplified AI workflow, coupled with a powerful CNN algorithm, achieves comparable BNE quantification accuracy to manual and feature-based analysis with substantial time savings, eliminating workflow complexity. This streamlined approach holds significant clinical value, enhancing IFC accessibility for routine diagnostic purposes.

BACKGROUND: Detection of del(17p) in myeloma is generally performed by fluorescence in situ hybridization (FISH) on a slide with analysis of up to 200 nuclei. The small cell sample analyzed makes this a low precision test. We report the utility of an automated FISH method, called \"immuno-flowFISH\", to detect plasma cells with adverse prognostic risk del(17p) in bone marrow and blood samples of patients with myeloma.
METHODS: Bone marrow (n = 31) and blood (n = 19) samples from 35 patients with myeloma were analyzed using immuno-flowFISH. Plasma cells were identified by CD38/CD138-immunophenotypic gating and assessed for the 17p locus and centromere of chromosome 17. Cells were acquired on an AMNIS ImageStreamX MkII imaging flow cytometer using INSPIRE software.
RESULTS: Chromosome 17 abnormalities were identified in CD38/CD138-positive cells in bone marrow (6/31) and blood (4/19) samples when the percent plasma cell burden ranged from 0.03% to 100% of cells. Abnormalities could be identified in 14.5%-100% of plasma cells.
CONCLUSIONS: The \"immuno-flowFISH\" imaging flow cytometric method could detect del(17p) in plasma cells in both bone marrow and blood samples of myeloma patients. This method was also able to detect gains and losses of chromosome 17, which are also of prognostic significance. The lowest levels of 0.009% (bone marrow) and 0.001% (blood) for chromosome 17 abnormalities was below the detection limit of current FISH method. This method offers potential as a new means of identifying these prognostically important chromosomal defects, even when only rare cells are present and for serial disease monitoring.