PXPermute reveals staining importance in multichannel imaging flow cytometry.

Imaging flow cytometry (IFC) allows rapid acquisition of numerous single-cell images per second, capturing information from multiple fluorescent channels. However, the traditional process of staining cells with fluorescently labeled conjugated antibodies for IFC analysis is time consuming, expensive, and potentially harmful to cell viability. To streamline experimental workflows and reduce costs, it is crucial to identify the most relevant channels for downstream analysis. In this study, we introduce PXPermute, a user-friendly and powerful method for assessing the significance of IFC channels, particularly for cell profiling. Our approach evaluates channel importance by permuting pixel values within each channel and analyzing the resulting impact on machine learning or deep learning models. Through rigorous evaluation of three multichannel IFC image datasets, we demonstrate PXPermute's potential in accurately identifying the most informative channels, aligning with established biological knowledge. PXPermute can assist biologists with systematic channel analysis, experimental design optimization, and biomarker identification.

In situ monitoring of toxic effects of algal toxin on cells by a novel microfluidic flow cytometry instrument.

Algal toxins produced by microalgae, such as domoic acid (DA)1, have toxic effects on humans. However, toxicity tests using mice only yield lethal doses of algal toxins without providing insights into the mechanism of action on cells. In this study, a fast segmentation of microfluidic flow cytometry cell images based on the bidirectional background subtraction (BBS)2 method was developed to get the visual evidence of apoptosis in both bright-field and fluorescence images. This approach enables mapping of changes in cell morphology and activity under algal toxins, allowing for fast (within 60 s) and automated biological detection. By combining microfluidics with flow cytometry, the intricate cellular-level reaction process can be observed in micro samples of 293 T cells and mouse spleen cells, offering potential for future in vitro experiments.

Identification and characterization of extracellular vesicles from red cells infected with Babesia divergens and Babesia microti.

Babesiosis is a zoonosis and an important blood-borne human parasitic infection that has gained attention because of its growing infection rate in humans by transfer from animal reservoirs. Babesia represents a potential threat to the blood supply because asymptomatic infections in man are common, and blood from such donors can cause severe disease in certain recipients. Extracellular vesicles (EVs) are vesicles released by cells that contain a complex mixture of proteins, lipids, glycans, and genetic information that have been shown to play important roles in disease pathogenesis and susceptibility, as well as cell-cell communication and immune responses. In this article, we report on the identification and characterization of EVs released from red blood cells (RBCs) infected by two major human Babesia species-Babesia divergens from in vitro culture and those from an in vivo B. microti mouse infection. Using nanoparticle tracking analysis, we show that there is a range of vesicle sizes from 30 to 1,000 nm, emanating from the Babesia-infected RBC. The study of these EVs in the context of hemoparasite infection is complicated by the fact that both the parasite and the host RBC make and release vesicles into the extracellular environment. However, the EV frequency is 2- to 10-fold higher in Babesia-infected RBCs than uninfected RBCs, depending on levels of parasitemia. Using parasite-specific markers, we were able to show that ~50%-60% of all EVs contained parasite-specific markers on their surface and thus may represent the specific proportion of EVs released by infected RBCs within the EV population. Western blot analysis on purified EVs from both in vivo and in vitro infections revealed several parasite proteins that were targets of the host immune response. In addition, microRNA analysis showed that infected RBC EVs have different microRNA signature from uninfected RBC EVs, indicating a potential role as disease biomarkers. Finally, EVs were internalized by other RBCs in culture, implicating a potential role for these vesicles in cellular communication. Overall, our study points to the multiple functional implications of EVs in Babesia-host interactions and support the potential that EVs have as agents in disease pathogenesis.

A weakly supervised deep learning approach for label-free imaging flow-cytometry-based blood diagnostics.

The application of machine learning approaches to imaging flow cytometry (IFC) data has the potential to transform the diagnosis of hematological diseases. However, the need for manually labeled single-cell images for machine learning model training has severely limited its clinical application. To address this, we present iCellCnn, a weakly supervised deep learning approach for label-free IFC-based blood diagnostics. We demonstrate the capability of iCellCnn to achieve diagnosis of Sézary syndrome (SS) from patient samples on the basis of bright-field IFC images of T cells obtained after fluorescence-activated cell sorting of human peripheral blood mononuclear cell specimens. With a sample size of four healthy donors and five SS patients, iCellCnn achieved a 100% classification accuracy. As iCellCnn is not restricted to the diagnosis of SS, we expect such weakly supervised approaches to tap the diagnostic potential of IFC by providing automatic data-driven diagnosis of diseases with so-far unknown morphological manifestations.

Quantitative phase imaging of cells in a flow cytometry arrangement utilizing Michelson interferometer-based off-axis digital holographic microscopy.

We combined Michelson-interferometer-based off-axis digital holographic microscopy (DHM) with a common flow cytometry (FCM) arrangement. Utilizing object recognition procedures and holographic autofocusing during the numerical reconstruction of the acquired off-axis holograms, sharply focused quantitative phase images of suspended cells in flow were retrieved without labeling, from which biophysical cellular features of distinct cells, such as cell radius, refractive index and dry mass, can be subsequently retrieved in an automated manner. The performance of the proposed concept was first characterized by investigations on microspheres that were utilized as test standards. Then, we analyzed two types of pancreatic tumor cells with different morphology to further verify the applicability of the proposed method for quantitative live cell imaging. The retrieved biophysical datasets from cells in flow are found in good agreement with results from comparative investigations with previously developed DHM methods under static conditions, which demonstrates the effectiveness and reliability of our approach. Our results contribute to the establishment of DHM in imaging FCM and prospect to broaden the application spectrum of FCM by providing complementary quantitative imaging as well as additional biophysical cell parameters which are not accessible in current high-throughput FCM measurements.

Kinetics of Mimivirus Infection Stages Quantified Using Image Flow Cytometry.

Due to the heterogeneity of viruses and their hosts, a comprehensive view of viral infection is best achieved by analyzing large populations of infected cells. However, information regarding variation in infected cell populations is lost in bulk measurements. Motivated by an interest in the temporal progression of events in virally infected cells, we used image flow cytometry (IFC) to monitor changes in Acanthamoeba polyphaga cells infected with Mimivirus. This first use of IFC to study viral infection required the development of methods to preserve morphological features of adherent amoeba cells prior to detachment and analysis in suspension. It also required the identification of IFC parameters that best report on key events in the Mimivirus infection cycle. The optimized IFC protocol enabled the simultaneous monitoring of diverse processes including generation of viral factories, transport, and fusion of replication centers within the cell, accumulation of viral progeny, and changes in cell morphology for tens of thousands of cells. After obtaining the time windows for these processes, we used IFC to evaluate the effects of perturbations such as oxidative stress and cytoskeletal disruptors on viral infection. Accurate dose-response curves could be generated, and we found that mild oxidative stress delayed multiple stages of virus production, but eventually infection processes occurred with approximately the same amplitudes. We also found that functional actin cytoskeleton is required for fusion of viral replication centers and later for the production of viral progeny. Through this report, we demonstrate that IFC offers a quantitative, high-throughput, and highly robust approach to study viral infection cycles and virus-host interactions. © The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.