Imaging flow cytometry: coping with heterogeneity in biological systems

Deformability of Heterogeneous Red Blood Cells in Aging and Related Pathologies

Aging is interrelated with changes in red blood cell parameters and functionality. In this article, we focus on red blood cells (RBCs) and provide a review of the known changes associated with the characterization of RBC deformability in aging and related pathologies. The biophysical parameters complement the commonly used biochemical parameters and may contribute to a better understanding of the aging process. The power of the deformability measurement approach is well established in clinical settings. Measuring RBCs' deformability has the advantage of relative simplicity, and it reflects the complex effects developing in erythrocytes during aging. However, aging and related pathological conditions also promote heterogeneity of RBC features and have a certain impact on the variance in erythrocyte cell properties. The possible applications of deformability as an early biophysical biomarker of pathological states are discussed, and modulating PIEZO1 as a therapeutic target is suggested. The changes in RBCs' shape can serve as a proxy for deformability evaluation, leveraging single-cell analysis with imaging flow cytometry and artificial intelligence algorithms. The characterization of biophysical parameters of RBCs is in progress in humans and will provide a better understanding of the complex dynamics of aging.

Accessible high-speed image-activated cell sorting

Over the past six decades, fluorescence-activated cell sorting (FACS) has become an essential technology for basic and clinical research by enabling the isolation of cells of interest in high throughput. Recent technological advancements have started a new era of flow cytometry. By combining the spatial resolution of microscopy with high-speed cell sorting, new instruments allow cell sorting based on simple image-derived parameters or sophisticated image analysis algorithms, thereby greatly expanding the scope of applications. In this review, we discuss the systems that are commercially available or have been described in enough methodological and engineering detail to allow their replication. We summarize their strengths and limitations and highlight applications that have the potential to transform various fields in basic life science research and clinical settings.

Shedding Light on Intracellular Proteins using Flow Cytometry

Intracellular protein abundance is routinely measured in mammalian cells using population-based techniques such as western blotting which fail to capture single cell protein levels or using fluorescence microscopy which is although suitable for single cell protein detection but not for rapid analysis of large no. of cells. Flow cytometry offers rapid, high-throughput, multiparameter-based analysis of intracellular protein expression in statistically significant no. of cells at single cell resolution. In past few decades, customized assays have been developed for flow cytometric detection of specific intracellular proteins. This review discusses the scope of flow cytometry for intracellular protein detection in mammalian cells along with specific applications. Technological advancements to overcome the limitations of traditional flow cytometry for the same are also discussed.

Molecular pixelation: spatial proteomics of single cells by sequencing

The spatial distribution of cell surface proteins governs vital processes of the immune system such as intercellular communication and mobility. However, fluorescence microscopy has limited scalability in the multiplexing and throughput needed to drive spatial proteomics discoveries at subcellular level. We present Molecular Pixelation (MPX), an optics-free, DNA sequence-based method for spatial proteomics of single cells using antibody-oligonucleotide conjugates (AOCs) and DNA-based, nanometer-sized molecular pixels. The relative locations of AOCs are inferred by sequentially associating them into local neighborhoods using the sequence-unique DNA pixels, forming >1,000 spatially connected zones per cell in 3D. For each single cell, DNA-sequencing reads are computationally arranged into spatial proteomics networks for 76 proteins. By studying immune cell dynamics using spatial statistics on graph representations of the data, we identify known and new patterns of spatial organization of proteins on chemokine-stimulated T cells, highlighting the potential of MPX in defining cell states by the spatial arrangement of proteins.

Dysregulation of cellular membrane homeostasis as a crucial modulator of cancer risk

Cellular membranes serve as an epicentre combining extracellular and cytosolic components with membranous effectors, which together support numerous fundamental cellular signalling pathways that mediate biological responses. To execute their functions, membrane proteins, lipids and carbohydrates arrange, in a highly coordinated manner, into well-defined assemblies displaying diverse biological and biophysical characteristics that modulate several signalling events. The loss of membrane homeostasis can trigger oncogenic signalling. More recently, it has been documented that select membrane active dietaries (MADs) can reshape biological membranes and subsequently decrease cancer risk. In this review, we emphasize the significance of membrane domain structure, organization and their signalling functionalities as well as how loss of membrane homeostasis can steer aberrant signalling. Moreover, we describe in detail the complexities associated with the examination of these membrane domains and their association with cancer. Finally, we summarize the current literature on MADs and their effects on cellular membranes, including various mechanisms of dietary chemoprevention/interception and the functional links between nutritional bioactives, membrane homeostasis and cancer biology.

Imaging Flow Cytometry: Development, Present Applications, and Future Challenges

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.

Flow cytometry: Aspects and application in plant and biological science

Flow cytometry is a potent method that enables the quick and concurrent investigation of several characteristics of single cells in solution. Photodiodes or photomultiplier tubes are employed to detect the dispersed and fluorescent light signals that are produced by the laser beam as it passes through the cells. Photodetectors transform the light signals produced by the laser into electrical impulses. A computer then analyses these electrical impulses to identify and measure the various cell populations depending on their fluorescence or light scattering characteristics. Based on their fluorescence or light scattering properties, cell populations can be examined and/or isolated. This review covers the basic principle, components, working and specific biological applications of flow cytometry, including studies on plant, cell and molecular biology and methods employed for data processing and interpretation as well as the potential future relevance of this methodology in light of retrospective analysis and recent advancements in flow cytometry.

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.

Label-free cell classification in holographic flow cytometry through an unbiased learning strategy

Nowadays, label-free imaging flow cytometry at the single-cell level is considered the stepforward lab-on-a-chip technology to address challenges in clinical diagnostics, biology, life sciences and healthcare. In this framework, digital holography in microscopy promises to be a powerful imaging modality thanks to its multi-refocusing and label-free quantitative phase imaging capabilities, along with the encoding of the highest information content within the imaged samples. Moreover, the recent achievements of new data analysis tools for cell classification based on deep/machine learning, combined with holographic imaging, are urging these systems toward the effective implementation of point of care devices. However, the generalization capabilities of learning-based models may be limited from biases caused by data obtained from other holographic imaging settings and/or different processing approaches. In this paper, we propose a combination of a Mask R-CNN to detect the cells, a convolutional auto-encoder, used to the image feature extraction and operating on unlabelled data, thus overcoming the bias due to data coming from different experimental settings, and a feedforward neural network for single cell classification, that operates on the above extracted features. We demonstrate the proposed approach in the challenging classification task related to the identification of drug-resistant endometrial cancer cells.

Cellaca® PLX image cytometer as an alternative for immunophenotyping, GFP/RFP transfection efficiencies, and apoptosis analysis

Cell and gene therapy is a fast-growing field for cancer therapeutics requiring reliable instrumentation and technologies. Key parameters essential for satisfying Chemistry Manufacturing and Controls criteria standards are routinely performed using flow cytometry. Recently, image cytometry was developed for cell characterization and cell-based assays but had not yet demonstrated sufficient sensitivity for surface marker detection. We developed the Cellaca® PLX image cytometry system and the respective methodologies required for immunophenotyping, GFP and RFP transfection/transduction efficiencies, and cell health analyses for routine cell characterization. All samples tested were compared directly to results from the CytoFLEX flow cytometer. PBMCs were stained with T-cell surface markers for immunophenotyping, and results show highly comparable CD3, CD4, and CD8 populations (within 5 %). GFP- or RFP-expressing cell lines were analyzed for transfection/transduction efficiencies, and the percentage positive cells and respective viabilities were equivalent on both systems. Staurosporine-treated Jurkat cells were stained for apoptotic markers, where annexin V and caspase-3 positive cells were within 5 % comparing both instruments. The proposed system may provide a complementary tool for performing routine cell-based experiments with improved efficiency and sensitivity compared to prior image cytometers, which may be significantly valuable to the cell and gene therapy field.

Global transcriptome analysis of Pseudomonas aeruginosa NT06 response to potassium chloride, sodium lactate, sodium citrate, and microaerophilic conditions in a fish ecosystem

Pseudomonas aeruginosa is an opportunistic pathogen that recently has been increasingly isolated from foods, especially from minimally processed fish-based products. Those are preserved by the addition of sodium chloride (NaCl) and packaging in a modified atmosphere. However, the current trends of minimizing NaCl content may result in an increased occurrence of P. aeruginosa. NaCl can be replaced with potassium chloride (KCl) or sodium salts of organic acids. Herein, we examined the antimicrobial effects of KCl, sodium lactate (NaL), sodium citrate (NaC), and sodium acetate (NaA) against P. aeruginosa NT06 isolated from fish. Transcriptome response of cells grown in medium imitating a fish product supplemented with KCl and KCl/NaL/NaC and maintained under microaerophilic conditions was analysed. Flow cytometry analysis showed that treatment with KCl and KCl/NaL/NaC resulted in changed metabolic activity of cells. In response to KCl and KCl/NaL/NaC treatment, genes related to cell maintenance, stress response, quorum sensing, virulence, efflux pump, and metabolism were differentially expressed. Collectively, our results provide an improved understanding of the response of P. aeruginosa to NaCl alternative compounds that can be implemented in fish-based products and encourage further exploration of the development of effective methods to protect foods against the P. aeruginosa, underestimate foodborne bacteria.

Detection and Quantification of Cryptococcus Uptake by Phagocytic Cells Using Imaging Flow Cytometry

Cryptococcus neoformans, the predominant etiological agent of cryptococcosis, is an encapsulated fungal pathogen found ubiquitously in the environment that causes pneumonia and life-threatening infections of the central nervous system. Following inhalation of yeasts or desiccated basidiospores into the lung alveoli, resident pulmonary phagocytic cells aid in the identification and eradication of Cryptococcus yeast through their arsenal of pattern recognition receptors (PRRs). PRRs recognize conserved pathogen-associated molecular patterns (PAMPs), such as branched mannans, β-glucans, and chitins that are the major components of the fungal cell wall. However, the key receptors/ligand interactions required for cryptococcal recognition and eventual fungal clearance have yet to be elucidated. Here we present an imaging flow cytometer (IFC) method that offers a novel quantitative cellular imaging and population statistics tool to accurately measure phagocytosis of fungal cells. It has the capacity to measure two distinct steps of phagocytosis: association/attachment and internalization in a high-throughput and quantitative manner that is difficult to achieve with other technologies. Results from these IFC studies allow for the potential to identify PRRs required for recognition, uptake, and subsequent activation of cytokine production, as well as other effector cell responses required for fungal clearance.

Flow Cytometry Quantification of Transient Transfections in Mammalian Cells

Flow cytometry is a powerful quantitative assay supporting high-throughput collection of single-cell data with a high dynamic range. For flow cytometry to yield reproducible data with a quantitative relationship to the underlying biology, however, requires that (1) appropriate process controls are collected along with experimental samples, (2) these process controls are used for unit calibration and quality control, and (3) data are analyzed using appropriate statistics. To this end, this chapter describes methods for quantitative flow cytometry through the addition of process controls and analyses, thereby enabling better development, modeling, and debugging of engineered biological organisms. The methods described here have specifically been developed in the context of transient transfections in mammalian cells but may in many cases be adaptable to other categories of transfection and other types of cells.

A Survey on Core Flow Cytometry Facilities: Instrument Maintenance, Usage, and Funding

Flow cytometry is a powerful tool that finds applications in various fields such as immunology, molecular biology, cancer biology, virology, and infectious disease monitoring. A significant portion of the research in these disciplines is supported by flow cytometry shared resource laboratories (SRLs). There are several types of flow cytometers available for use in SRLs, including analyzers, sorters, imaging flow cytometers, and mass cytometers. Each type has different challenges when it comes to maintenance and life expectancy. An independent online survey was conducted to better understand instrument maintenance and turnover in flow cytometry SRLs. Questions regarding instrument uptime (availability), its usage, routine maintenance, and cost associated with it were addressed. The respondents also answered questions pertaining to the frequency of deep cleaning of the instrument and quality control. In addition, the survey queried about the source of funding used to purchase the instruments and possible reasons for a replacement. Presented herein are the results compiled from 146 core facilities that provide a look at the operation within a typical SRL, with the responses reflecting researchers' experiences with handling flow cytometers.

Impact of Mycobacterium tuberculosis Glycolipids on the CD4+ T Cell-Macrophage Immunological Synapse

Mycobacterium tuberculosis cell-wall glycolipids such as mannosylated lipoarabinomannan (ManLAM) can inhibit murine CD4+ T cells by blocking TCR signaling. This results in suppression of IL-2 production, reduced T cell proliferation, and induction of CD4+ T cell anergy. This study extended these findings to the interaction between primary human CD4+ T cells and macrophages infected by mycobacteria. Exposure of human CD4+ T cells to ManLAM before activation resulted in loss of polyfunctionality, as measured by IL-2, IFN-γ, and TNF-α expression, and reduced CD25 expression. This was not associated with upregulation of inhibitory receptors CTLA-4, PD-1, TIM-3, and Lag-3. By confocal microscopy and imaging flow cytometry, ManLAM exposure reduced conjugate formation between macrophages and CD4+ T cells. ManLAM colocalized to the immunological synapse (IS) and reduced translocation of lymphocyte-specific protein tyrosine kinase (LCK) to the IS. When CD4+ T cells and Mycobacterium bovis BCG-infected monocytes were cocultured, ManLAM colocalized to CD4+ T cells, which formed fewer conjugates with infected monocytes. These results demonstrate that mycobacterial cell-wall glycolipids such as ManLAM can traffic from infected macrophages to disrupt productive IS formation and inhibit CD4+ T cell activation, contributing to immune evasion by M. tuberculosis.

Label-Free Intracellular Multi-Specificity in Yeast Cells by Phase-Contrast Tomographic Flow Cytometry

In-flow phase-contrast tomography provides a 3D refractive index of label-free cells in cytometry systems. Its major limitation, as with any quantitative phase imaging approach, is the lack of specificity compared to fluorescence microscopy, thus restraining its huge potentialities in single-cell analysis and diagnostics. Remarkable results in introducing specificity are obtained through artificial intelligence (AI), but only for adherent cells. However, accessing the 3D fluorescence ground truth and obtaining accurate voxel-level co-registration of image pairs for AI training is not viable for high-throughput cytometry. The recent statistical inference approach is a significant step forward for label-free specificity but remains limited to cells' nuclei. Here, a generalized computational strategy based on a self-consistent statistical inference to achieve intracellular multi-specificity is shown. Various subcellular compartments (i.e., nuclei, cytoplasmic vacuoles, the peri-vacuolar membrane area, cytoplasm, vacuole-nucleus contact site) can be identified and characterized quantitatively at different phases of the cells life cycle by using yeast cells as a biological model. Moreover, for the first time, virtual reality is introduced for handling the information content of multi-specificity in single cells. Full fruition is proofed for exploring and interacting with 3D quantitative biophysical parameters of the identified compartments on demand, thus opening the route to a metaverse for 3D microscopy.

A live-cell platform to isolate phenotypically defined subpopulations for spatial multi-omic profiling

Numerous techniques have been employed to deconstruct the heterogeneity observed in normal and diseased cellular populations, including single cell RNA sequencing, in situ hybridization, and flow cytometry. While these approaches have revolutionized our understanding of heterogeneity, in isolation they cannot correlate phenotypic information within a physiologically relevant live-cell state with molecular profiles. This inability to integrate a live-cell phenotype-such as invasiveness, cell:cell interactions, and changes in spatial positioning-with multi-omic data creates a gap in understanding cellular heterogeneity. We sought to address this gap by employing lab technologies to design a detailed protocol, termed Spatiotemporal Genomic and Cellular Analysis (SaGA), for the precise imaging-based selection, isolation, and expansion of phenotypically distinct live cells. This protocol requires cells expressing a photoconvertible fluorescent protein and employs live cell confocal microscopy to photoconvert a user-defined single cell or set of cells displaying a phenotype of interest. The total population is then extracted from its microenvironment, and the optically highlighted cells are isolated using fluorescence activated cell sorting. SaGA-isolated cells can then be subjected to multi-omics analysis or cellular propagation for in vitro or in vivo studies. This protocol can be applied to a variety of conditions, creating protocol flexibility for user-specific research interests. The SaGA technique can be accomplished in one workday by non-specialists and results in a phenotypically defined cellular subpopulations for integration with multi-omics techniques. We envision this approach providing multi-dimensional datasets exploring the relationship between live cell phenotypes and multi-omic heterogeneity within normal and diseased cellular populations.

Characterization of Circulating Tumor Cells Using Imaging Flow Cytometry in Liver Disease Patients

Background

Hepatocellular carcinoma (HCC) is asymptomatic at an early stage which delays its timely diagnosis and treatment. Circulating tumor cells (CTCs), derived from a primary or secondary tumor, may help in the management of HCC. Here, we evaluate and characterize CTCs in liver disease patients.

Methods

In total, 65 patients, categorized into liver cirrhosis (LC) (n = 30) and HCC (n = 35), were enrolled. Using ImagestreamX MkII imaging flow cytometer, CTCs were detected and characterized using biomarker expression of EpCAM, CK, AFP, CD45, and DRAQ5 in LC and HCC patients.

Results

CTCs were detected in 33/35 (94%) HCC patients and in 28/30 (93%) LC patients. In the HCC group, the number of biomarker-positive CTCs was higher in BCLC stage D when compared with others. EpCAM + CK was the most expressed biomarker on CTCs in LC versus HCC (83.3% vs. 77.14%), followed by AFP (80% vs. 65.71%), EpCAM (30% vs. 28.57%), and CK (16.6% vs. 14.28%). The EpCAM cell area was significantly associated (P value = 0.031) with the CTC-positive status. The combination biomarker expression of CTCs cell area (EpCAM, CK, and AFP) performed well with the area under the curve of 0.92, high sensitivity, and specificity in detecting early-stage and AFP-negative HCC as well as in AFP-negative LC cases.

Conclusion

Enumeration and cell area of CTCs may be used as a biomarker for early detection of HCC and guiding treatment.

Development of an Imaging Flow Cytometry Method for Fungal Cytological Profiling and Its Potential Application in Antifungal Drug Development

Automated imaging techniques have been in increasing demand for the more advanced analysis and efficient characterization of cellular phenotypes. The success of the image-based profiling method hinges on assays that can rapidly and simultaneously capture a wide range of phenotypic features. We have developed an automated image acquisition method for fungal cytological profiling (FCP) using an imaging flow cytometer that can objectively measure over 250 features of a single fungal cell. Fungal cells were labeled with calcofluor white and FM4-64FX, which bind to the cell wall and lipophilic membrane, respectively. Images of single cells were analyzed using IDEAS^® software. We first acquired FCPs of fungal cells treated with fluconazole, amphotericin B, and caspofungin, each with a distinct mode of action, to establish FCP databases of profiles associated with specific antifungal treatment. Once fully established, we investigated the potential application of this technique as a screening methodology to identify compounds with novel antifungal activity against Candida albicans and Cryptococcus neoformans. Altogether, we have developed a rapid, powerful, and novel image-profiling method for the phenotypic characterization of fungal cells, also with potential applications in antifungal drug development.

Bioaffinity Nanoprobes for Foodborne Pathogen Sensing

Bioaffinity nanoprobes are a type of biosensor that utilize the specific binding properties of biological molecules, such as antibodies, enzymes, and nucleic acids, for the detection of foodborne pathogens. These probes serve as nanosensors and can provide highly specific and sensitive detection of pathogens in food samples, making them an attractive option for food safety testing. The advantages of bioaffinity nanoprobes include their ability to detect low levels of pathogens, rapid analysis time, and cost-effectiveness. However, limitations include the need for specialized equipment and the potential for cross-reactivity with other biological molecules. Current research efforts focus on optimizing the performance of bioaffinity probes and expanding their application in the food industry. This article discusses relevant analytical methods, such as surface plasmon resonance (SPR) analysis, Fluorescence Resonance Energy Transfer (FRET) measurements, circular dichroism, and flow cytometry, that are used to evaluate the efficacy of bioaffinity nanoprobes. Additionally, it discusses advances in the development and application of biosensors in monitoring foodborne pathogens.

Interaction of Pexiganan (MSI-78)-Derived Analogues Reduces Inflammation and TLR4-Mediated Cytokine Secretion: A Comparative Study

Antibiotic-resistant bacterial infections have increased the prevalence of sepsis and septic shock mortality worldwide and have become a global concern. Antimicrobial peptides (AMPs) show remarkable properties for developing new antimicrobial agents and host response modulatory therapies. A new series of AMPs derived from pexiganan (MSI-78) were synthesized. The positively charged amino acids were segregated at their N- and C-termini, and the rest of the amino acids created a hydrophobic core surrounded by positive charges and were modified to simulate the lipopolysaccharide (LPS). The peptides were investigated for their antimicrobial activity and LPS-induced cytokine release inhibition profile. Various biochemical and biophysical methods were used, including attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, microscale thermophoresis (MST), and electron microscopy. Two new AMPs, MSI-Seg-F2F and MSI-N7K, preserved their neutralizing endotoxin activity while reducing toxicity and hemolytic activity. Combining all of these properties makes the designed peptides potential candidates to eradicate bacterial infection and detoxify LPS, which might be useful for sepsis treatment.

Imaging Flow Cytometry for High-Throughput Phenotyping of Synthetic Cells

The reconstitution of basic cellular functions in micrometer-sized liposomes has led to a surge of interest in the construction of synthetic cells. Microscopy and flow cytometry are powerful tools for characterizing biological processes in liposomes with fluorescence readouts. However, applying each method separately leads to a compromise between information-rich imaging by microscopy and statistical population analysis by flow cytometry. To address this shortcoming, we here introduce imaging flow cytometry (IFC) for high-throughput, microscopy-based screening of gene-expressing liposomes in laminar flow. We developed a comprehensive pipeline and analysis toolset based on a commercial IFC instrument and software. About 60 thousands of liposome events were collected per run starting from one microliter of the stock liposome solution. Robust population statistics from individual liposome images was performed based on fluorescence and morphological parameters. This allowed us to quantify complex phenotypes covering a wide range of liposomal states that are relevant for building a synthetic cell. The general applicability, current workflow limitations, and future prospects of IFC in synthetic cell research are finally discussed.

Recent advances in microfluidics for single-cell functional proteomics

Single-cell proteomics (SCP) reveals phenotypic heterogeneity by profiling individual cells, their biological states and functional outcomes upon signaling activation that can hardly be probed via other omics characterizations. This has become appealing to researchers as it enables an overall more holistic view of biological details underlying cellular processes, disease onset and progression, as well as facilitates unique biomarker identification from individual cells. Microfluidic-based strategies have become methods of choice for single-cell analysis because they allow facile assay integrations, such as cell sorting, manipulation, and content analysis. Notably, they have been serving as an enabling technology to improve the sensitivity, robustness, and reproducibility of recently developed SCP methods. Critical roles of microfluidics technologies are expected to further expand rapidly in advancing the next phase of SCP analysis to reveal more biological and clinical insights. In this review, we will capture the excitement of the recent achievements of microfluidics methods for both targeted and global SCP, including efforts to enhance the proteomic coverage, minimize sample loss, and increase multiplexity and throughput. Furthermore, we will discuss the advantages, challenges, applications, and future prospects of SCP.

Volumetric imaging of human mesenchymal stem cells (hMSCs) for non-destructive quantification of 3D cell culture growth

The adoption of cell-based therapies into the clinic will require tremendous large-scale expansion to satisfy future demand, and bioreactor-microcarrier cultures are best suited to meet this challenge. The use of spherical microcarriers, however, precludes in-process visualization and monitoring of cell number, morphology, and culture health. The development of novel expansion methods also motivates the advancement of analytical methods used to characterize these microcarrier cultures. A robust optical imaging and image-analysis assay to non-destructively quantify cell number and cell volume was developed. This method preserves 3D cell morphology and does not require membrane lysing, cellular detachment, or exogenous labeling. Complex cellular networks formed in microcarrier aggregates were imaged and analyzed in toto. Direct cell enumeration of large aggregates was performed in toto for the first time. This assay was successfully applied to monitor cellular growth of mesenchymal stem cells attached to spherical hydrogel microcarriers over time. Elastic scattering and fluorescence lightsheet microscopy were used to quantify cell volume and cell number at varying spatial scales. The presented study motivates the development of on-line optical imaging and image analysis systems for robust, automated, and non-destructive monitoring of bioreactor-microcarrier cell cultures.

Data-driven microscopy allows for automated context-specific acquisition of high-fidelity image data

Light microscopy is a powerful single-cell technique that allows for quantitative spatial information at subcellular resolution. However, unlike flow cytometry and single-cell sequencing techniques, microscopy has issues achieving high-quality population-wide sample characterization while maintaining high resolution. Here, we present a general framework, data-driven microscopy (DDM) that uses real-time population-wide object characterization to enable data-driven high-fidelity imaging of relevant phenotypes based on the population context. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As a proof of concept, we develop and apply DDM with plugins for improved high-content screening and live adaptive microscopy for cell migration and infection studies that capture events of interest, rare or common, with high precision and resolution. We propose that DDM can reduce human bias, increase reproducibility, and place single-cell characteristics in the context of the sample population when interpreting microscopy data, leading to an increase in overall data fidelity.

Flow Cytometry with Anti-Diffraction Light Sheet (ADLS) by Spatial Light Modulation

Flow cytometry is a widespread and powerful technique whose resolution is determined by its capacity to accurately distinguish fluorescently positive populations from negative ones. However, most informative results are discarded while performing the measurements of conventional flow cytometry, e.g., the cell size, shape, morphology, and distribution or location of labeled exosomes within the unpurified biological samples. Herein, we propose a novel approach using an anti-diffraction light sheet with anisotroic feature to excite fluorescent tags. Constituted by an anti-diffraction Bessel-Gaussian beam array, the light sheet is 12 μm wide, 12 μm high, and has a thickness of ~0.8 μm. The intensity profile of the excited fluorescent signal can, therefore, reflect the size and allow samples in the range from O (100 nm) to 10 μm (e.g., blood cells) to be transported via hydrodynamic focusing in a microfluidic chip. The sampling rate is 500 kHz, which provides a capability of high throughput without sacrificing the spatial resolution. Consequently, the proposed anti-diffraction light sheet flow cytometry (ADLSFC) can obtain more informative results than the conventional methodologies, and is able to provide multiple characteristics (e.g., the size and distribution of fluorescent signal) helping to distinguish the target samples from the complex backgrounds.

The steep road to nonviral nanomedicines: Frequent challenges and culprits in designing nanoparticles for gene therapy

The potential of therapeutically loaded nanoparticles (NPs) has been successfully demonstrated during the last decade, with NP-mediated nonviral gene delivery gathering significant attention as highlighted by the broad clinical acceptance of mRNA-based COVID-19 vaccines. A significant barrier to progress in this emerging area is the wild variability of approaches reported in published literature regarding nanoparticle characterizations. Here, we provide a brief overview of the current status and outline important concerns regarding the need for standardized protocols to evaluate NP uptake, NP transfection efficacy, drug dose determination, and variability of nonviral gene delivery systems. Based on these concerns, we propose wide adherence to multimodal, multiparameter, and multistudy analysis of NP systems. Adoption of these proposed approaches will ensure improved transparency, provide a better basis for interlaboratory comparisons, and will simplify judging the significance of new findings in a broader context, all critical requirements for advancing the field of nonviral gene delivery.

Pluripotent stem cells: Basic biology or else differentiations aimed at translational research and the role of flow cytometry

Pluripotent stem cell research has revolutionized the modern era for the past 14 years with the advent of induced pluripotent stem cells. Before this time, scientists had access to human and mouse embryonic stem cells primarily for basic research and an attempt towards lineage-specific differentiations for cell therapy applications. Regarding pluripotent stem cells, expression of bonafide marker proteins such as Oct4, Nanog, Sox2, Klf4, c-Myc, and Lin28 have been considered giving a perfect readout for pluripotent stem cells and assessed using an analytical flow cytometer. In addition to the intracellular markers, surface markers such as stage-specific embryonic antigen-1 for mouse cells and SSEA-4 for human cells are needed to sort pure populations of stem cells for further downstream applications for cell therapy. The surface marker SSEA-4 is the most appropriate for obtaining pure populations of human pluripotent stem cells. When differentiated in a controlled manner using growth factors or small molecules, it is mandatory to assess the downregulation of pluripotency markers (Oct4, Nanog, Sox2, and Klf4) with subsequent up-regulation of stage-specific differentiation markers. Such assessments are done using flow cytometry. Pluripotent stem cells have a high teratoma-forming potential in vivo. Small amounts of undifferentiated PSCs might lead to dangerous teratomas upon transplantation if leftover in the pool of differentiated cells. Hence, flow cytometry is essential for sorting out PSC populations with teratoma-forming potential. The pure populations of differentiated progenitors need to be flow-sorted before differentiating them further for cell therapy applications. For example, Glycoprotein 2 is a specific cell-surface marker for pancreatic progenitors that enables one to sort the pancreatic progenitors differentiated from human PSCs. Taken together, analytical flow cytometry, and cell sorting provide indispensable tools in PSC research and cell therapy.

A live-cell platform to isolate phenotypically defined subpopulations for spatial multi-omic profiling

Numerous techniques have been employed to deconstruct the heterogeneity observed in normal and diseased cellular populations, including single cell RNA sequencing, in situ hybridization, and flow cytometry. While these approaches have revolutionized our understanding of heterogeneity, in isolation they cannot correlate phenotypic information within a physiologically relevant live-cell state, with molecular profiles. This inability to integrate a historical live-cell phenotype, such as invasiveness, cell:cell interactions, and changes in spatial positioning, with multi-omic data, creates a gap in understanding cellular heterogeneity. We sought to address this gap by employing lab technologies to design a detailed protocol, termed Spatiotemporal Genomics and Cellular Analysis (SaGA), for the precise imaging-based selection, isolation, and expansion of phenotypically distinct live-cells. We begin with cells stably expressing a photoconvertible fluorescent protein and employ live cell confocal microscopy to photoconvert a user-defined single cell or set of cells displaying a phenotype of interest. The total population is then extracted from its microenvironment, and the optically highlighted cells are isolated using fluorescence activated cell sorting. SaGA-isolated cells can then be subjected to multi-omics analysis or cellular propagation for in vitro or in vivo studies. This protocol can be applied to a variety of conditions, creating protocol flexibility for user-specific research interests. The SaGA technique can be accomplished in one workday by non-specialists and results in a phenotypically defined cellular subpopulation for integration with multi-omics techniques. We envision this approach providing multi-dimensional datasets exploring the relationship between live-cell phenotype and multi-omic heterogeneity within normal and diseased cellular populations.

Quantifying Golgi Apparatus Fragmentation Using Imaging Flow Cytometry

Unlike the common conception of the Golgi apparatus as a static organelle, it is, in fact, a dynamic structure, as well as a sensitive sensor for the cellular status. In response to various stimuli, the intact Golgi structure undergoes fragmentation. This fragmentation can yield either partial fragmentation, resulting in several separated chunks, or complete vesiculation of the organelle. These distinct morphologies form the basis of several methods for the quantification of the Golgi status. In this chapter, we describe our imaging flow cytometry-based method for quantifying changes in the Golgi architecture. This method has all the benefits of imaging flow cytometry-namely, it is rapid, high-throughput, and robust-while affording easy implementation and analysis capabilities.

Identifying Bacterial Lineages in Salmonella by Flow Cytometry

Advances in technologies that permit high-resolution analysis of events in single cells have revealed that phenotypic heterogeneity is a widespread phenomenon in bacteria. Flow cytometry has the potential to describe the distribution of cellular properties within a population of bacterial cells and has yielded invaluable information about the ability of isogenic cells to diversify into phenotypic subpopulations. This review will discuss several single-cell approaches that have recently been applied to define phenotypic heterogeneity in populations of Salmonella enterica.

Glioma Stem Cells: Novel Data Obtained by Single-Cell Sequencing

Glioma is the most common type of primary CNS tumor, composed of cells that resemble normal glial cells. Recent genetic studies have provided insight into the inter-tumoral heterogeneity of gliomas, resulting in the updated 2021 WHO classification of gliomas. Thorough understanding of inter-tumoral heterogeneity has already improved the prognosis and treatment outcomes of some types of gliomas. Currently, the challenge for researchers is to study the intratumoral cell heterogeneity of newly defined glioma subtypes. Cancer stem cells (CSCs) present in gliomas and many other tumors are an example of intratumoral heterogeneity of great importance. In this review, we discuss the modern concept of glioma stem cells and recent single-cell sequencing-driven progress in the research of intratumoral glioma cell heterogeneity. The particular emphasis was placed on the recently revealed variations of the cell composition of the subtypes of the adult-type diffuse gliomas, including astrocytoma, oligodendroglioma and glioblastoma. The novel data explain the inconsistencies in earlier glioma stem cell research and also provide insight into the development of more effective targeted therapy and the cell-based immunotherapy of gliomas. Separate sections are devoted to the description of single-cell sequencing approach and its role in the development of cell-based immunotherapies for glioma.

Calibration-Aimed Comparison of Image-Cytometry- and Flow-Cytometry-Based Approaches of Ploidy Analysis

Ploidy analysis is the fundamental method of measuring DNA content. For decades, the principal way of conducting ploidy analysis was through flow cytometry. A flow cytometer is a specialized tool for analyzing cells in a solution. This is convenient in laboratory environments, but prohibits measurement reproducibility and the complete detachment of sample preparation from data acquisition and analysis, which seems to have become paramount with the constant decrease in the number of pathologists per capita all over the globe. As more open computer-aided systems emerge in medicine, the demand for overcoming these shortcomings, and opening access to even more (and more flexible) options, has also emerged. Image-based analysis systems can provide an alternative to these types of workloads, placing the abovementioned problems in a different light. Flow cytometry data can be used as a reference for calibrating an image-based system. This article aims to show an approach to constructing an image-based solution for ploidy analysis, take measurements for a basic comparison of the data produced by the two methods, and produce a workflow with the ultimate goal of calibrating the image-based system.

Simple Detection of Unstained Live Senescent Cells with Imaging Flow Cytometry

Cellular senescence is a hallmark of aging and a promising target for therapeutic approaches. The identification of senescent cells requires multiple biomarkers and complex experimental procedures, resulting in increased variability and reduced sensitivity. Here, we propose a simple and broadly applicable imaging flow cytometry (IFC) method. This method is based on measuring autofluorescence and morphological parameters and on applying recent artificial intelligence (AI) and machine learning (ML) tools. We show that the results of this method are superior to those obtained measuring the classical senescence marker, senescence-associated beta-galactosidase (SA-β-Gal). We provide evidence that this method has the potential for diagnostic or prognostic applications as it was able to detect senescence in cardiac pericytes isolated from the hearts of patients affected by end-stage heart failure. We additionally demonstrate that it can be used to quantify senescence “in vivo” and can be used to evaluate the effects of senolytic compounds. We conclude that this method can be used as a simple and fast senescence assay independently of the origin of the cells and the procedure to induce senescence.

Application of flow cytometry in transfusion medicine: The Sanjay Gandhi Post Graduate Institute of Medical Sciences, India experience

The application of flow cytometry (FC) is diverse and this powerful tool in used in multiple disciplines such as molecular biology, immunology, cancer biology, virology, and infectious disease screening. FC analyzes a single cell or a particle very rapidly as they flow past single or multiple lasers while suspended in buffered solution. FC has a great impact in the field of transfusion medicine (TM) due to its ability to analyze individual cell population and cell epitopes by sensitive, reproducible, and objective methodologies. The main uses of FC in TM are detection of fetomaternal hemorrhage, diagnosis of paroxysmal nocturnal hemoglobinuria, quantification of D antigen, detection of platelet antibody, quality control of blood components, for example, residual leukocyte counts and evaluation of CD34-positive hematopoietic progenitor cells in stem cell grafts. In recent years, FC has been implemented as an alternative method for the detection and characterization of red cell autoantibodies in autoimmune hemolytic anemia. Many workers considered FC as a very good complement when aberrant expression of various erythrocyte antigens needs to be elucidated. It has been extensively used in the resolution of ABO discrepancies and chimerism study. FC has also been used successfully in various platelet immunological studies. In the recent past, FC has been used in several studies to assess the platelet storage lesions and elucidate granulocyte/monocyte integrity and immunology. FC analysis of CD34+ stem cells is now the method of choice to determine the dosage of the collected progenitor cells. The technique is vastly used to evaluate residual leukocytes in leukodepleted blood components. We conclude that flow cytometers are becoming smaller, cheaper, and more user-friendly and are available in many routine laboratories. FC represents a highly innovative technique for many common diagnostic and scientific fields in TM. Finally, it is the tool of choice to develop and optimize new cellular and immunotherapeutic trials.

Overcoming chemoresistance to b-raf inhibitor in melanoma via targeted inhibition of phosphoenolpyruvate carboxykinase1 using 3-mercaptopropionic acid

The resistance of melanoma to BRAF inhibitors remains a tough clinical challenge. In order to explore the underlying mechanism of drug resistance in melanoma, we established the resistant cell line to vemurafenib, and assessed the changes of drug-resistant cells on proliferation, apoptosis, oxidative stress and tumor stemness. Our results suggest that phosphoenolpyruvate carboxykinase1 (PCK1) is activated and inhibits the oxidative stress caused by vemurafenib in drug-resistant cells. Long term treatment of vemurafenib increases the expression of PCK1 which reduces the production of reactive oxygen species (ROS) by activating PI3K/Akt pathway. After the inhibition of PCK1 by 3-mercaptopropionic acid (3-MPA), the therapeutic sensitivity of vemurafenib is restored. In conclusion, this study disclosed that drug-resistant cells appeared to regulate their own proliferation, oxidative stress and tumor dryness by activating Akt/PCK1/ROS pathway, and shed new insights into acquiring drug resistance in melanoma.

Growth Characteristic Analysis of Haematococcus pluvialis in a Microfluidic Chip Using Digital in-Line Holographic Flow Cytometry

In order to obtain high yield of astaxanthin, a high-value compound with ultrastrong antioxidant capacity, it is necessary to identify the growth characteristics (biomass, morphology, and size) of Haematococcus pluvialis. The current detection methods have the disadvantages of labor-consuming operation or complicated measurement system. It is an urgent need to explore a simple and cost-effective method for the detection of H. pluvialis with large size distribution during its growth period. In this work, a digital in-line holographic flow cytometry using a linear array sensor is proposed to measure the growth characteristics of H. pluvialis in a two-dimensional (2-D) hydrodynamic focusing microfluidic chip. Based on the modified angular spectrum method, the distorting holograms caused by the asynchrony of sample flow velocity and acquisition speed of the linear array sensor were rectified and reconstructed. In addition, the depth-of-focus of the imaging system were digitally extended to cover the entire depth of the microfluidic channel for optimized imaging quality. We have utilized the proposed method to statistically investigate the biomass, morphology and size of H. pluvialis under different culture conditions and growth durations.

Imaging Flow Cytometry as a Quick and Effective Identification Technique of Pollen Grains from Betulaceae, Oleaceae, Urticaceae and Asteraceae

Despite the continuous and intensive development of laboratory techniques, a light microscope is still the most common tool used in pollen grains differentiation. However, microscopy is time-consuming and needs well-educated and experienced researchers. Other currently used techniques can be categorised as images and non-images analysis, but each has certain limitations. We propose a new approach to differentiate pollen grains using the Imaging Flow Cytometry (IFC) technique. It allows for high-throughput fluorescence data recording, which, in contrast to the standard FC, also enables real-time control of the results thanks to the possibility of digital image recording of cells flowing through the measuring capillary. The developed method allows us to determine the characteristics of the pollen grains population based on the obtained fluorescence data, using various combinations of parameters available in the IDEAS software, which can be analysed on different fluorescence channels. On this basis, we distinguished pollen grains both between and within different genera belonging to the Betulaceae, Oleaceae, Urticaceae and Asteraceae families. Thereby, we prove that the proposed methodology is sufficient for accurate, fast, and cost-effective identification and potentially can be used in the routine analysis of allergenic pollen grains.

Theranostic Interpolation of Genomic Instability in Breast Cancer

Breast cancer is a diverse disease caused by mutations in multiple genes accompanying epigenetic aberrations of hazardous genes and protein pathways, which distress tumor-suppressor genes and the expression of oncogenes. Alteration in any of the several physiological mechanisms such as cell cycle checkpoints, DNA repair machinery, mitotic checkpoints, and telomere maintenance results in genomic instability. Theranostic has the potential to foretell and estimate therapy response, contributing a valuable opportunity to modify the ongoing treatments and has developed new treatment strategies in a personalized manner. “Omics” technologies play a key role while studying genomic instability in breast cancer, and broadly include various aspects of proteomics, genomics, metabolomics, and tumor grading. Certain computational techniques have been designed to facilitate the early diagnosis of cancer and predict disease-specific therapies, which can produce many effective results. Several diverse tools are used to investigate genomic instability and underlying mechanisms. The current review aimed to explore the genomic landscape, tumor heterogeneity, and possible mechanisms of genomic instability involved in initiating breast cancer. We also discuss the implications of computational biology regarding mutational and pathway analyses, identification of prognostic markers, and the development of strategies for precision medicine. We also review different technologies required for the investigation of genomic instability in breast cancer cells, including recent therapeutic and preventive advances in breast cancer.

Liquid biopsy in lung cancer management

Liquid biopsy is a promising tool for a better cancer management and currently opens perspectives for several clinical applications, such as detection of mutations when the analysis from tissue is not available, monitoring tumor mutational burden and prediction of targeted therapy response. These characteristics validate liquid biopsy analysis as a strong cancer biomarkers source with high potential for improving cancer patient's evolution. Compared to classical biopsy, liquid biopsy is a minimal invasive procedure, and it allows the real-time monitoring of treatment response. Considering that lung cancer is the most common cause of cancer-associated death worldwide and that only 15-19% of the lung cancer patients survive five years after diagnosis, there is an important interest in improving its management. Like in other types of solid cancers, lung cancer could benefit from liquid biopsy through a simple peripheral blood sample as tumor-related biomarkers, such as circulating tumor cells (CTCs), cell-free nucleic acids (cfNA) [cell-free ribonucleic acid (cfRNA) and cell-free deoxyribonucleic acid (cfDNA)], exosomes and tumor-educated platelets (TEPs) may shed into circulation because of necrosis or in an active manner. More, the detection and analysis of these biomarkers could lead to a better understanding of oncological diseases like lung cancer. The better the tumor profile is established; the better management is possible. However, this approach has currently some limitations, such as low cfNA concentration or low count of CTCs that might be overcome by improving the actual methods and technologies.

Reduced acoustic resonator dimensions improve focusing efficiency of bacteria and submicron particles

In this study, we demonstrate an acoustofluidic device that enables single-file focusing of submicron particles and bacteria using a two-dimensional (2D) acoustic standing wave. The device consists of a 100 μm × 100 μm square channel that supports 2D particle focusing in the channel center at an actuation frequency of 7.39 MHz. This higher actuation frequency compared with conventional bulk acoustic systems enables radiation-force-dominant motion of submicron particles and overcomes the classical size limitation (≈2 μm) of acoustic focusing. We present acoustic radiation force-based focusing of particles with diameters less than 0.5 μm at a flow rate of 12 μL min^-1, and 1.33 μm particles at flow rates up to 80 μL min^-1. The device focused 0.25 μm particles by the 2D acoustic radiation force while undergoing a channel cross-section centered, single-vortex acoustic streaming. A suspension of bacteria was also investigated to evaluate the biological relevance of the device, which demonstrated the alignment of bacteria in the channel at a flow rate of up to 20 μL min^-1. The developed acoustofluidic device can align submicron particles within a narrow flow stream in a highly robust manner, validating its use as a flow-through focusing chamber to perform high-throughput and accurate flow cytometry of submicron objects.

Imaging Flow Cytometry to Study Biofilm-Associated Microbial Aggregates

The aim of the research was to design an advanced analytical tool for the precise characterization of microbial aggregates from biofilms formed on food-processing surfaces. The approach combined imaging flow cytometry with a machine learning-based interpretation protocol. Biofilm samples were collected from three diagnostic points of the food-processing lines at two independent time points. The samples were investigated for the complexity of microbial aggregates and cellular metabolic activity. Thus, aggregates and singlets of biofilm-associated microbes were simultaneously examined for the percentages of active, mid-active, and nonactive (dead) cells to evaluate the physiology of the microbial cells forming the biofilm structures. The tested diagnostic points demonstrated significant differences in the complexity of microbial aggregates. The significant percentages of the bacterial aggregates were associated with the dominance of active microbial cells, e.g., 75.3% revealed for a mushroom crate. This confirmed the protective role of cellular aggregates for the survival of active microbial cells. Moreover, the approach enabled discriminating small and large aggregates of microbial cells. The developed tool provided more detailed characteristics of bacterial aggregates within a biofilm structure combined with high-throughput screening potential. The designed methodology showed the prospect of facilitating the detection of invasive biofilm forms in the food industry environment.

Imaging flow cytometry data analysis using convolutional neural network for quantitative investigation of phagocytosis

Macrophages play an important role in the adaptive immune system. Their ability to neutralize cellular targets through Fc receptor-mediated phagocytosis has relied upon immunotherapy that has become of particular interest for the treatment of cancer and autoimmune diseases. A detailed investigation of phagocytosis is the key to the improvement of the therapeutic efficiency of existing medications and the creation of new ones. A promising method for studying the process is imaging flow cytometry (IFC) that acquires thousands of cell images per second in up to 12 optical channels and allows multiparametric fluorescent and morphological analysis of samples in the flow. However, conventional IFC data analysis approaches are based on a highly subjective manual choice of masks and other processing parameters that can lead to the loss of valuable information embedded in the original image. Here, we show the application of a Faster region-based convolutional neural network (CNN) for accurate quantitative analysis of phagocytosis using imaging flow cytometry data. Phagocytosis of erythrocytes by peritoneal macrophages was chosen as a model system. CNN performed automatic high-throughput processing of datasets and demonstrated impressive results in the identification and classification of macrophages and erythrocytes, despite the variety of shapes, sizes, intensities, and textures of cells in images. The developed procedure allows determining the number of phagocytosed cells, disregarding cases with a low probability of correct classification. We believe that CNN-based approaches will enable powerful in-depth investigation of a wide range of biological processes and will reveal the intricate nature of heterogeneous objects in images, leading to completely new capabilities in diagnostics and therapy.

Single-Cell Technologies to Study Phenotypic Heterogeneity and Bacterial Persisters

Antibiotic persistence is a phenomenon in which rare cells of a clonal bacterial population can survive antibiotic doses that kill their kin, even though the entire population is genetically susceptible. With antibiotic treatment failure on the rise, there is growing interest in understanding the molecular mechanisms underlying bacterial phenotypic heterogeneity and antibiotic persistence. However, elucidating these rare cell states can be technically challenging. The advent of single-cell techniques has enabled us to observe and quantitatively investigate individual cells in complex, phenotypically heterogeneous populations. In this review, we will discuss current technologies for studying persister phenotypes, including fluorescent tags and biosensors used to elucidate cellular processes; advances in flow cytometry, mass spectrometry, Raman spectroscopy, and microfluidics that contribute high-throughput and high-content information; and next-generation sequencing for powerful insights into genetic and transcriptomic programs. We will further discuss existing knowledge gaps, cutting-edge technologies that can address them, and how advances in single-cell microbiology can potentially improve infectious disease treatment outcomes.

Imaging Flow Cytometry and Confocal Immunofluorescence Microscopy of Virus-Host Cell Interactions

Viruses are diverse pathogens that use host factors to enter cells and cause disease. Imaging the entry and replication phases of viruses and their interactions with host factors is key to fully understanding viral infections. This review will discuss how confocal microscopy and imaging flow cytometry are used to investigate virus entry and replication mechanisms in fixed and live cells. Quantification of viral images and the use of cryo-electron microscopy to gather structural information of viruses is also explored. Using imaging to understand how viruses replicate and interact with host factors, we gain insight into cellular processes and identify novel targets to develop antiviral therapeutics and vaccines.

Single Cell Analysis of Stored Red Blood Cells Using Ultra-High Throughput Holographic Cytometry

Holographic cytometry is introduced as an ultra-high throughput implementation of quantitative phase imaging of single cells flowing through parallel microfluidic channels. Here, the approach was applied for characterizing the morphology of individual red blood cells during storage under regular blood bank conditions. Samples from five blood donors were examined, over 100,000 cells examined for each, at three time points. The approach allows high-throughput phase imaging of a large number of cells, greatly extending our ability to study cellular phenotypes using individual cell images. Holographic cytology images can provide measurements of multiple physical traits of the cells, including optical volume and area, which are observed to consistently change over the storage time. In addition, the large volume of cell imaging data can serve as training data for machine-learning algorithms. For the study here, logistic regression was used to classify the cells according to the storage time points. The analysis showed that at least 5000 cells are needed to ensure accuracy of the classifiers. Overall, results showed the potential of holographic cytometry as a diagnostic tool.

The Evolution of Single-Cell Analysis and Utility in Drug Development

This review provides a brief history of the advances of cellular analysis tools focusing on instrumentation, detection probes, and data analysis tools. The interplay of technological advancement and a deeper understanding of cellular biology are emphasized. The relevance of this topic to drug development is that the evaluation of cellular biomarkers has become a critical component of the development strategy for novel immune therapies, cell therapies, gene therapies, antiviral therapies, and vaccines. Moreover, recent technological advances in single-cell analysis are providing more robust cellular measurements and thus accelerating the advancement of novel therapies.

Graphical abstract

Progressive use of multispectral imaging flow cytometry in various research areas

Multi-spectral imaging flow cytometry (MIFC) has become one of the most powerful technologies for investigating general analytics, molecular and cell biology, biotechnology, medicine, and related fields. It combines the capabilities of the morphometric and photometric analysis of single cells and micrometer-sized particles in flux with regard to thousands of events. It has become the tool of choice for a wide range of research and clinical applications. By combining the features of flow cytometry and fluorescence microscopy, it offers researchers the ability to couple the spatial resolution of multicolour images of cells and organelles with the simultaneous analysis of a large number of events in a single system. This provides the opportunity to visually confirm findings and collect novel data that would otherwise be more difficult to obtain. This has led many researchers to design innovative assays to gain new insight into important research questions. To date, it has been successfully used to study cell morphology, surface and nuclear protein co-localization, protein-protein interactions, cell signaling, cell cycle, cell death, and cytotoxicity, intracellular calcium, drug uptake, pathogen internalization, and other applications. Herein we describe some of the recent advances in the field of multiparametric imaging flow cytometry methods in various research areas.