High-throughput Single-cell Analysis Research Articles

High-throughput single cell analysis using multi-focus Raman spectroscopy under randomly interleaved scattering projection

Raman microspectroscopy is a powerful tool for label-free monitoring of single-cell dynamics. However, the traditional single-point acquisition mode is extremely inefficient because it only analyzes one cell at a time. We propose a method that combines multi-focus Raman excitation with random interleaving of scattering projections. The approach uses a time-sharing multi-focus array to simultaneously excite multiple biological cells and synchronously projects their Raman spectra into a single spectral channel with varying shifts. The spectral shifts of the cells are randomly interleaved during data acquisition, resulting in a sequence of mixed spectra from which a compressive sensing method is utilized to reconstruct the time-lapse Raman spectra of the individual cells. The method’s feasibility and performance are validated by numerical modeling and experimental investigations into biological spore germination. The results indicated that the throughput of single cell analysis can be increased by up to a factor of 15. The reconstructed spectra of the individual cells demonstrated exceptional fidelity, faithfully capturing the cellular changes in the individual cells. The developed technology paves the way towards high-throughput, label-free monitoring of living single cells, which has promising applications in cell biology.

Rapid flowing cells localization enabled by spatiotemporal manipulation of their holographic patterns.

Lab-on-a-Chip microfluidic devices present an innovative and cost-effective platform in the current trend of miniaturization and simplification of imaging flow cytometry; they are excellent candidates for high-throughput single-cell analysis. In such microfluidic platforms, cell tracking becomes a fundamental tool for investigating biophysical processes, from intracellular dynamics to the characterization of cell motility and migration. However, high-throughput and long-term cell tracking puts a high demand on the consumption of computing resources. Here, we propose a novel strategy to achieve rapid 3D cell localizations along the microfluidic channel. This method is based on the spatiotemporal manipulation of recorded holographic interference fringes, and it allows fast and precise localization of cells without performing complete holographic reconstruction. Conventional holographic tracking is typically based on the phase contrast obtained by decoupling the calculation of optical axial and transverse coordinates. Computing time and resource consumption may increase because all the frames need to be calculated in the Fourier domain. In our proposed method, the 2D transverse positions are directly located by morphological calculation based on the hologram. The complex-amplitude wavefronts are directly reconstructed by spatiotemporal phase shifting to calculate the axial position by the refocusing criterion. Only spatial calculation is considered in the proposed method. We demonstrate that the computational time of transverse tracking is only one-tenth of the conventional method, while the total computational time of the proposed method decreases up to 54% with respect to the conventional approach. The proposed approach can open the route for analyzing flow cytometry in quantitative phase microscopy assays.

Comparison of Multiple Machine Learning Models for the Classification of Cell States Based on Impedance Features

Abstract Microfluidic impedance flow cytometry (IFC) enables high-throughput single-cell analysis in label-free manner. Tens of thousands of cells can be measured in several minutes under multiple frequencies, which give rise to impedance features with rich information ideal for machine learning (ML)-based cell classification. Conventional data processing approach for IFC typically exploits the scattered distribution of the measured cells which correlates the impedance features (e.g., the impedance amplitude and phase at different frequencies, the amplitude ratio between high to low frequencies) and exhibits resolved cell clusters in scatter plot. By manually gating on the distributed dots plot, the cell subgroups get mapped to different cell type or cellular states. ML-based data processing for IFC not only reduces the human workload, and more importantly, it also eliminates the human interference to manual gating strategy, and thus potentially leading to more concise and accurate cell classification results. Here, we demonstrate the ML-based classification of different cell states for tumor cells subject to anticancer drug treatment. IFC-measured impedance data of H1650 cells and Hela cells under drug-induced mitosis block state and apoptosis state have been applied for ML-based cell state identification. Three machine learning models, including the random forest (RF), support vector machine (SVM) and K-nearest neighbours (KNN) have been trained for impedance features extracted from cell signals at both 500 kHz and 10 MHz. In comparison, the RF model give rise to the highest classification accuracies among all trained models here. For H1650 cells, 84.01% and 85.96% accuracies have been respectively achieved for G1/S state vs. apoptosis and G2/M vs. apoptosis. For the classification between G2/M vs. apoptosis for the paclitaxel-treated Hela cells, the RF model produces high accuracy of 98.70%.

Label-free evanescent imaging of cellular heterogeneity in membrane protein binding kinetics

Quantifying cellular heterogeneity of membrane protein binding kinetics is challenging but important for exploring drug resistance and screening drugs. Label-free analysis methods have emerged as promising tools for in situ binding kinetics analysis, but they have not been used for high throughput single cell analysis in live cells. Here we show that this is possible with Evanescent Scattering Microscopy (ESM). The ESM permits analyzing the kinetics of ligand binding onto membrane proteins in individual fixed and live cells, and provides a throughput of ∼200 cells in a single measurement with a period of ∼7 minutes. The statistical analysis further shows that the dissociation rate constant dominates the heterogeneity of cell responses to ligand binding, providing evidence for a long-standing hypothesis that the drug-target residence time may play a critical role in drug treatment. In addition, the ESM reveals that under some conditions the cells have responses to drug binding at the single cell level, whereas the ensemble measurements may average out the individual differences and present false negative results. We anticipate that the new evanescent imaging method will provide a powerful tool to quantify the functions of cellular proteins, especially their cell-to-cell heterogeneity that can provide fuel for drug resistance.

SciMET-cap: high-throughput single-cell methylation analysis with a reduced sequencing burden

DNA methylation is a key component of the mammalian epigenome, playing a regulatory role in development, disease, and other processes. Robust, high-throughput single-cell DNA methylation assays are now possible (sciMET); however, the genome-wide nature of DNA methylation results in a high sequencing burden per cell. Here, we leverage target enrichment with sciMET to capture sufficient information per cell for cell type assignment using substantially fewer sequence reads (sciMET-cap). Accumulated off-target coverage enables genome-wide differentially methylated region (DMR) calling for clusters with as few as 115 cells. We characterize sciMET-cap on human PBMCs and brain (middle frontal gyrus).

Microfluidic Biochips for Single-Cell Isolation and Single-Cell Analysis of Multiomics and Exosomes.

Single-cell multiomic and exosome analyses are potent tools in various fields, such as cancer research, immunology, neuroscience, microbiology, and drug development. They facilitate the in-depth exploration of biological systems, providing insights into disease mechanisms and aiding in treatment. Single-cell isolation, which is crucial for single-cell analysis, ensures reliable cell isolation and quality control for further downstream analyses. Microfluidic chips are small lightweight systems that facilitate efficient and high-throughput single-cell isolation and real-time single-cell analysis on- or off-chip. Therefore, most current single-cell isolation and analysis technologies are based on the single-cell microfluidic technology. This review offers comprehensive guidance to researchers across different fields on the selection of appropriate microfluidic chip technologies for single-cell isolation and analysis. This review describes the design principles, separation mechanisms, chip characteristics, and cellular effects of various microfluidic chips available for single-cell isolation. Moreover, this review highlights the implications of using this technology for subsequent analyses, including single-cell multiomic and exosome analyses. Finally, the current challenges and future prospects of microfluidic chip technology are outlined for multiplex single-cell isolation and multiomic and exosome analyses.

HIV/HBV coinfection remodels the immune landscape and natural killer cell ADCC functional responses.

HBV and HIV coinfection is a common occurrence globally, with significant morbidity and mortality. Both viruses lead to immune dysregulation including changes in natural killer (NK) cells, a key component of antiviral defense and a promising target for HBV cure strategies. Here we used high-throughput single-cell analysis to explore the immune cell landscape in people with HBV mono-infection and HIV/HBV coinfection, on antiviral therapy, with emphasis on identifying the distinctive characteristics of NK cell subsets that can be therapeutically harnessed. Our data show striking differences in the transcriptional programs of NK cells. HIV/HBV coinfection was characterized by an over-representation of adaptive, KLRC2 -expressing NK cells, including a higher abundance of a chemokine-enriched ( CCL3/CCL4 ) adaptive cluster. The NK cell remodeling in HIV/HBV coinfection was reflected in enriched activation pathways, including CD3ζ phosphorylation and ZAP-70 translocation that can mediate stronger antibody-dependent cellular cytotoxicity responses and a bias toward chemokine/cytokine signaling. By contrast, HBV mono-infection imposed a stronger cytotoxic profile on NK cells and a more prominent signature of "exhaustion" with higher circulating levels of HBsAg. Phenotypic alterations in the NK cell pool in coinfection were consistent with increased "adaptiveness" and better capacity for antibody-dependent cellular cytotoxicity compared to HBV mono-infection. Overall, an adaptive NK cell signature correlated inversely with circulating levels of HBsAg and HBV-RNA in our cohort. This study provides new insights into the differential signature and functional profile of NK cells in HBV and HIV/HBV coinfection, highlighting pathways that can be manipulated to tailor NK cell-focused approaches to advance HBV cure strategies in different patient groups.

Increasing Fluid Viscosity Ensures Consistent Single-Cell Encapsulation.

High-throughput single-cell analysis typically relies on the isolation of cells of interest in separate compartments for subsequent phenotypic or genotypic characterization. Using microfluidics, this is achieved by isolating individual cells in microdroplets or microwells. However, due to cell-to-cell variability in size, shape, and density, the cell capture efficiencies may vary significantly. This variability can negatively impact the measurements and introduce undesirable artifacts when trying to isolate and characterize heterogeneous cell populations. In this study, we show that single-cell isolation biases in microfluidics can be circumvented by increasing the viscosity of fluids in which cells are dispersed. At a viscosity of 40-50 cP (cP), the cell sedimentation is effectively reduced, resulting in a steady cell flow inside the microfluidics chip and consistent encapsulation in water-in-oil droplets over extended periods of time. This approach allows nearly all cells in a sample to be isolated with the same efficiency, irrespective of their type. Our results show that increased fluid viscosity, rather than cell-adjusted density, provides a more reliable approach to mitigate single-cell isolation biases.

Abstract 1210: High-throughput time-resolved single-cell analysis of T-cell activation

Abstract How immune cells respond to stimuli is fundamental in developing adoptive cell therapies, vaccines, and other immunotherapies. T cells dynamically undergo activation, exhaustion, expansion, phenotype switching, and cell killing. Flow cytometry is the gold standard method for characterizing these at high throughput, typically by comparing T cell populations before and at different timepoints after treatment. This approach is limited to population-level analyses and overlooks any heterogeneity of individual T cell responses over time, which may contribute to variability in response. Here, we describe the development of a novel flow cytometry-based method (Time-lapse flowTM) to characterize immune cell responses over time at the single cell level. To measure the same cells repeatedly on a flow cytometer, we employed a new optical barcoding technology called laser particles (LPs) that provide individual cells with a unique spectral signature that can be tracked over the course of multiple measurements. Using a custom flow cytometer with fluorescence and LP detection, we performed a multi-day assay to capture how T cell marker expression changes over time after CD3/CD28 activation. Specifically, we used releasable antibodies to quantify PD1, CD69 and CD25 at each time-point and correlated these expression kinetics with cell function at the end of the assay by measuring cytokine secretion (IFNg, IL2, TNFa) after PMA/ionomycin stimulation. We found significant cellular heterogeneity in kinetic profiles of PD1, CD69 and CD25 expression, with different dominant ‘kinetic modes’ present for each marker. For example, while a large proportion of PD1 expression was transient (positive on day 1, but not on days 2 and 3), nearly all cells that express CD69 on day 1 retained their expression on day 2. We also found that T cells with late expression of PD1 had significantly more cytokine secretion compared to cells that were initially positive for PD1. These results were replicated with at least 3 replicate samples for multiple donors. Differences in kinetic phenotypes were not explained by conventional phenotyping with CD4, CD8, CD45RA and CCR7. To date, immune cell phenotyping with flow cytometry involves classifying cell types through their marker expression profiles at a single time-point. Here, we introduce a novel approach for acquiring time-resolved single-cell data with a flow cytometer and applied it to study T cell activation. We found novel kinetic phenotypes of T cells defined by their marker expression over time, which also correlated to their functional potential (cytokine secretion). These kinetic phenotypes could not be identified with conventional flow cytometry. Our approach could have significant implications for immunotherapy development, particularly in identifying the most suitable cell populations to use, understanding mechanisms of exhaustion and persistence, and elucidating the basis of donor variability. Citation Format: Sheldon J. Kwok, Sarah Forward, Emane Rose Assita, Trevor Brown, Marissa Fahlberg, Pratip Chattopadhyay. High-throughput time-resolved single-cell analysis of T-cell activation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1210.

SecMet-FISH: labeling, visualization, and enumeration of secondary metabolite producing microorganisms.

Our understanding of the role of secondary metabolites in microbial communities is challenged by intrinsic limitations of culturing bacteria under laboratory conditions and hence cultivation independent approaches are needed. Here, we present a protocol termed Secondary Metabolite FISH (SecMet-FISH), combining advantages of gene-targeted fluorescence in situ hybridization (geneFISH) with in-solution methods (in-solution FISH) to detect and quantify cells based on their genetic capacity to produce secondary metabolites. The approach capitalizes on the conserved nature of biosynthetic gene clusters (BGCs) encoding adenylation (AD) and ketosynthase (KS) domains, and thus selectively targets the genetic basis of non-ribosomal peptide and polyketide biosynthesis. The concept relies on the generation of amplicon pools using degenerate primers broadly targeting AD and KS domains followed by fluorescent labeling, detection, and quantification. Initially, we obtained AD and KS amplicons from Pseuodoalteromonas rubra, which allowed us to successfully label and visualize BGCs within P. rubra cells, demonstrating the feasibility of SecMet-FISH. Next, we adapted the protocol and optimized it for hybridization in both Gram-negative and Gram-positive bacterial cell suspensions, enabling high-throughput single cell analysis by flow cytometry. Ultimately, we used SecMet-FISH to successfully distinguish secondary metabolite producers from non-producers in a five-member synthetic community.

Multiplexed fluorescence and scatter detection with single cell resolution using on-chip fiber optics for droplet microfluidic applications

Droplet microfluidics has emerged as a critical component of several high-throughput single-cell analysis techniques in biomedical research and diagnostics. Despite significant progress in the development of individual assays, multiparametric optical sensing of droplets and their encapsulated contents has been challenging. The current approaches, most commonly involving microscopy-based high-speed imaging of droplets, are technically complex and require expensive instrumentation, limiting their widespread adoption. To address these limitations, we developed the OptiDrop platform; this platform is a novel optofluidic setup that leverages the principles of flow cytometry. Our platform enables on-chip detection of the scatter and multiple fluorescence signals from the microfluidic droplets and their contents using optical fibers. The highly customizable on-chip optical fiber-based signal detection system enables simplified, miniaturized, low-cost, multiparametric sensing of optical signals with high sensitivity and single-cell resolution within each droplet. To demonstrate the ability of the OptiDrop platform, we conducted a differential expression analysis of the major histocompatibility complex (MHC) protein in response to IFNγ stimulation. Our results showed the platform’s ability to sensitively detect cell surface biomarkers using fluorescently labeled antibodies. Thus, the OptiDrop platform combines the versatility of flow cytometry with the power of droplet microfluidics to provide wide-ranging, scalable optical sensing solutions for research and diagnostics.

Practical 10-Color T-Cell Panel for Phenotyping Diverse Populations Using Spectral Flow Cytometry: A Beginner's Guide.

Flow cytometry stands as the most employed high-throughput single-cell analysis technique, facilitating the profiling of remarkably diverse samples, such as blood, bone marrow and body fluids. In addition, it allows for the discrimination of diverse immune cell subsets, including infrequently encountered types like T regulatory cells and exhausted CD28Null T cells. However, analyzing rare immune cell subsets with conventional flow cytometry poses challenges stemming from factors like fluorophore overlap, compensation issues, and limited flexibility in fluorophore selection. Therefore, spectral flow cytometry offers advantages over traditional flow cytometry. It measures the full emission spectrum and then separates it to identify different fluorochromes. This enables the use of fluorochromes with significant overlap in a single test, allowing for the analysis of more protein markers. Following this, spectral technology employs precise calculations to separate individual fluorochromes, thereby enabling the detection and elimination of autofluorescent signals originating from cells within the entire emission spectrum. This capability is pivotal in achieving deep phenotyping of immune cells with the requisite sensitivity and resolution essential for monitoring the immune systems of patients with compromised immunity, such as cancer and autoimmune disorders. Additionally, it allows for the exploration of interactions between distinct immune subsets. In this context, we introduce an optimized protocol utilizing spectral flow cytometry for precise T-cell characterization and differentiation, encompassing the assessment of their activation states. Furthermore, this protocol extends its applicability to the identification of less common circulating T-cell populations, notably T-regulatory and CD28Null T cells, following autofluorescence correction within the spectrum. This protocol provides a set of steps and reagents for the surface and intracellular staining of human T cells using whole peripheral blood. The spectral-based design of this panel allows for its applicability to other spectral machines, providing a versatile and efficient tool for T-cell analysis. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Achieving optimal staining through effective antibody titration Basic Protocol 2: Single-cell staining Basic Protocol 3: Comprehensive panel staining post-titration and spectral library integration.

Label-free single-cell analysis in microdroplets using a light-scattering-based optofluidic chip

Droplet-based single-cell analysis is a very powerful tool for studying phenotypic and genomic heterogeneity at single-cell resolution for a variety of biological problems. In conventional two-phase droplet microfluidics, due to the mismatch in optical properties between oil and aqueous phases, light scattering mainly happens at the oil/water interface that disables light-scattering-based cell analysis confined in microdroplets. Detection and analysis of cells in microdroplets thus mostly rely on the fluorescence labeling of cell samples, which may suffer from complex operation, cytotoxicity, and low fluorescence stability. In this work, we propose a novel light-scattering-based droplet screening (LSDS) that can effectively detect and characterize single cells confined in droplets by adjusting the optical properties of droplets in a multiangle optofluidic chip. Theoretical and simulated calculations suggest that refractive index (RI) matching in droplet two-phase materials can reduce or eliminate droplets’ scattered signals (background signal), enabling the differentiation of scattered signals from single cells and particles within droplets. Furthermore, by using a set of multiangle (from −145° to 140°) optical fibers integrated into the optofluidic chip, the scattered light properties of droplets with the RI ranging from 1.334 to 1.429 are measured. We find that the smaller the RI and size of microparticles inside droplets are, the smaller the RI difference between two-phase materials Δn is required. Especially, when Δn is smaller than 0.02, single cells in droplets can be detected and analyzed solely based on light scattering. This capability allows to accurately detect droplets containing one single cell and one single gel bead, a typical droplet encapsulation for single-cell sequencing. Altogether, this work provides a powerful platform for high-throughput label-free single-cell analysis in microdroplets for diverse single-cell related biological assays.

Trends in Biopharmaceutical Analysis: A Focus on Integrating Single-Cell Omics with Microfluidic Chips

Biopharmaceutical analysis is a rapidly evolving field that requires the development of new technologies and methods to keep pace with the increasing complexity of biologics. One of the most promising areas of research is the use of single-cell omics and microfluidic chips for the analysis of biopharmaceuticals. Single-cell omics has revolutionized our understanding of cellular heterogeneity, while microfluidic chips have enabled high-throughput analysis of single cells that provide an understanding of the complex biological network that complements the genomics and transcriptomics studies. This article will explore some of the emerging trends and technologies in biopharmaceutical analysis, with a particular focus on single-cell omics and microfluidic chips. We will also discuss the developments in ambient ionization mass spectrometry such as sub nanoampere ionization and the potential of low current ionization in studying cell-to-cell heterogeneity and its role in metabolomics.

Advances in Single-Cell Techniques for Linking Phenotypes to Genotypes.

Single-cell analysis has become an essential tool in modern biological research, providing unprecedented insights into cellular behavior and heterogeneity. By examining individual cells, this approach surpasses conventional population-based methods, revealing critical variations in cellular states, responses to environmental cues, and molecular signatures. In the context of cancer, with its diverse cell populations, single-cell analysis is critical for investigating tumor evolution, metastasis, and therapy resistance. Understanding the phenotype-genotype relationship at the single-cell level is crucial for deciphering the molecular mechanisms driving tumor development and progression. This review highlights innovative strategies for selective cell isolation based on desired phenotypes, including robotic aspiration, laser detachment, microraft arrays, optical traps, and droplet-based microfluidic systems. These advanced tools facilitate high-throughput single-cell phenotypic analysis and sorting, enabling the identification and characterization of specific cell subsets, thereby advancing therapeutic innovations in cancer and other diseases.

Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis.

Cells are the fundamental building blocks of living systems, exhibiting significant heterogeneity. The transcriptome connects the cellular genotype and phenotype, and profiling single-cell transcriptomes is critical for uncovering distinct cell types, states, and the interplay between cells in development, health, and disease. Nevertheless, single-cell transcriptome analysis faces daunting challenges due to the low abundance and diverse nature of RNAs in individual cells, as well as their heterogeneous expression. The advent and continuous advancements of next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies have solved these problems and facilitated the high-throughput, sensitive, full-length, and rapid profiling of single-cell RNAs. In this review, we provide a broad introduction to current methodologies for single-cell transcriptome sequencing. First, state-of-the-art advancements in high-throughput and full-length single-cell RNA sequencing (scRNA-seq) platforms using NGS are reviewed. Next, TGS-based long-read scRNA-seq methods are summarized. Finally, a brief conclusion and perspectives for comprehensive single-cell transcriptome analysis are discussed.

Viscoelastic-Sorting Integrated Deformability Cytometer for High-Throughput Sorting and High-Precision Mechanical Phenotyping of Tumor Cells.

The counts and phenotypes of circulating tumor cells (CTCs) in whole blood are useful for disease monitoring and prognostic assessment of cancer. However, phenotyping CTCs in the blood is difficult due to the presence of a large number of background blood cells, especially some blood cells with features similar to those of tumor cells. Herein, we presented a viscoelastic-sorting integrated deformability cytometer (VSDC) for high-throughput label-free sorting and high-precision mechanical phenotyping of tumor cells. A sorting chip for removing large background blood cells and a detection chip for detecting multiple cellular mechanical properties were integrated into our VSDC. Our VSDC has a sorting efficiency and a purity of over 95% and over 81% for tumor cells, respectively. Furthermore, multiple mechanical parameters were used to distinguish tumor cells from white blood cells using machine learning. An accuracy of over 97% for identifying tumor cells was successfully achieved with the highest identification accuracy of 99.4% for MCF-7 cells. It is envisioned that our VSDC will open up new avenues for high-throughput and label-free single-cell analysis in various biomedical applications.

High-throughput single-cell analysis reveals progressive mitochondrial DNA mosaicism throughout life.

Heteroplasmic mitochondrial DNA (mtDNA) mutations are a major cause of inherited disease and contribute to common late-onset human disorders. The late onset and clinical progression of mtDNA-associated disease is thought to be due to changing heteroplasmy levels, but it is not known how and when this occurs. Performing high-throughput single-cell genotyping in two mouse models of human mtDNA disease, we saw unanticipated cell-to-cell differences in mtDNA heteroplasmy levels that emerged prenatally and progressively increased throughout life. Proliferating spleen cells and nondividing brain cells had a similar single-cell heteroplasmy variance, implicating mtDNA or organelle turnover as the major force determining cell heteroplasmy levels. The two different mtDNA mutations segregated at different rates with no evidence of selection, consistent with different rates of random genetic drift in vivo, leading to the accumulation of cells with a very high mutation burden at different rates. This provides an explanation for differences in severity seen in human diseases caused by similar mtDNA mutations.

Interaction of Cellular Uptake of Nanosilver and Metallothionein Stress Expression Elucidated by 2D Single-Cell Analyses Based on LIF and ICP-MS.

The exploration of cytology mechanisms of nanosilver uptake, toxicity, and detoxification has become an important issue due to its widespread applications. Previous studies have shown differences in the toxic response of mammalian cells to nanosilver. However, the analysis results based on cell populations ignore the impact of cell uptake heterogeneity on the expression of associated stress proteins and cellular physiological activities. In this respect, this work investigated the interaction between silver uptake and metallothionein (MT) expression in individual cells. In addition, we have also preliminarily elucidated the sensitivity variation to AgNPs by using five cell lines, e.g., LX-2, HepG-2, SK-HEP-1, Huh-7, and MDA-MB-231, by adopting a two-dimensional (2D) high-throughput single-cell analysis platform coupling laser-induced fluorescence (LIF) and inductively coupled plasma mass spectrometry (ICP-MS). We developed a 2D data analysis method for one-to-one unification of fluorescence-mass spectrometry signals corresponding to a specific single cell. It indicated that there is no obvious correlation between cellular silver uptake and cell size, and the low MT expression of cells is more sensitive to silver nanoparticles. For each cell line, significant heterogeneity in MT expression was observed. This provides important information for understanding the potential heterogeneous effects of nanosilver on mammalian biological systems. Overall, detoxified cells are more tolerant to nanosilver and normal cells are more tolerant than cancer cells.

Smartphone Imaging Flow Cytometry for High-Throughput Single-Cell Analysis.

We present a portable imaging flow cytometer comprising a smartphone, a small-footprint optical framework, and a PDMS-based microfluidic device. Flow cytometric analysis is performed in a sheathless manner via elasto-inertial focusing with a custom-written Android program, integrating a graphical user interface (GUI) that provides a high degree of user control over image acquisition. The proposed system offers two different operational modes. First, "post-processing" mode enables particle/cell sizing at throughputs of up to 67 000 particles/s. Alternatively, "real-time" mode allows for integrated cell/particle classification with machine learning at throughputs of 100 particles/s. To showcase the efficacy of our platform, polystyrene particles are accurately enumerated within heterogeneous populations using the post-processing mode. In real-time mode, an open-source machine learning algorithm is deployed within a custom-developed Android application to classify samples containing cells of similar size but with different morphologies. The flow cytometer can extract high-resolution bright-field images with a spatial resolution <700 nm using the developed machine learning-based algorithm, achieving classification accuracies of 97% and 93% for Jurkat and EL4 cells, respectively. Our results confirm that the smartphone imaging flow cytometer (sIFC) is capable of both enumerating single particles in flow and identifying morphological features with high resolution and minimal hardware.