Development of microfluidic platform capable of high-throughput absolute quantification of single-cell multiple intracellular proteins from tumor cell lines and patient tumor samples

Computational Hyperspectral Microflow Cytometry

Miniaturized flow cytometry has significant potential for portable applications, such as cell-based diagnostics and the monitoring of therapeutic cell manufacturing, however, the performance of current techniques is often limited by the inability to resolve spectrally-overlapping fluorescence labels. Here, the study presents a computational hyperspectral microflow cytometer (CHC) that enables accurate discrimination of spectrally-overlapping fluorophores labeling single cells. CHC employs a dispersive optical element and an optimization algorithm to detect the full fluorescence emission spectrum from flowing cells, with a high spectral resolution of ≈3 nm in the range from 450 to 650 nm. CHC also includes a dedicated microfluidic device that ensures in-focus imaging through viscoelastic sheathless focusing, thereby enhancing the accuracy and reliability of microflow cytometry analysis. The potential of CHC for analyzing T lymphocyte subpopulations and monitoring changes in cell composition during T cell expansion is demonstrated. Overall, CHC represents a major breakthrough in microflow cytometry and can facilitate its use for immune cell monitoring.

Multiplex fluorescence detection of single-cell droplet microfluidics and its application in quantifying protein expression levels

This study presented a platform of multiplex fluorescence detection of single-cell droplet microfluidics with demonstrative applications in quantifying protein expression levels. The platform of multiplex fluorescence detection mainly included optical paths adopted from conventional microscopy enabling the generation of three optical spots from three laser sources for multiple fluorescence excitation and capture of multiple fluorescence signals by four photomultiplier tubes. As to platform characterization, microscopic images of three optical spots were obtained where clear Gaussian distributions of intensities without skewness confirmed the functionality of the scanning lens, while the controllable distances among three optical spots validated the functionality of fiber collimators and the reflector lens. As to demonstration, this platform was used to quantify single-cell protein expression within droplets where four-type protein expression of α-tubulin, Ras, c-Myc, and β-tubulin of CAL 27 (Ncell = 1921) vs WSU-HN6 (Ncell = 1881) were quantitatively estimated, which were (2.85 ± 0.72) × 105 vs (4.83 ± 1.58) × 105, (3.69 ± 1.41) × 104 vs (5.07 ± 2.13) × 104, (5.90 ± 1.45) × 104 vs (9.57 ± 2.85) × 104, and (3.84 ± 1.28) × 105 vs (3.30 ± 1.10) × 105, respectively. Neural pattern recognition was utilized for the classification of cell types, achieving successful rates of 69.0% (α-tubulin), 75.4% (Ras), 89.1% (c-Myc), 65.8% (β-tubulin), and 99.1% in combination, validating the capability of this platform of multiplex fluorescence detection to quantify various types of single-cell proteins, which could provide comprehensive evaluations on cell status.

Development of a Microfluidic Flow Cytometer with a Uniform Optical Field (Uni-μFCM) Enabling Quantitative Analysis of Single-Cell Proteins and Its Applications in Leukemia Gating, Tumor Classification, and Hierarchy of Cancer Stem Cells

Fast and quantitative estimation of single-cell proteins with various distribution patterns remains a technical challenge. Here, a microfluidic flow cytometer with a uniform optical field (Uni-μFCM) was developed, which enabled the translation of multicolor fluorescence signals of bound antibodies into targeted protein numbers with arbitrary distributions of biological cells. As the core of Uni-μFCM, a uniform optical field for optical excitation and fluorescence detection was realized by adopting a microfabricated metal window to shape the optical beam for excitation, which was modeled and validated by both numerical simulation and experimental characterization. After the validation of Uni-μFCM in single-cell protein quantification by measuring single-cell expressions of three transcriptional factors from four cell lines of variable sizes and origins, Uni-μFCM was applied to (1) quantify membrane and cytoplasmic markers of myeloid and lymphocytic leukocytes to classify cell lines and normal and patient blood samples; (2) measure single-cell expressions of key cytokines affiliated with gene stabilities, differentiating paired oral and colon tumor cell lines with varied malignancies, and (3) quantify single-cell stemming markers of liver tumor cell lines, cell subtypes, and liver patient samples to determine a variety of lineage hierarchy. By quantitatively assessing complex cellular phenotypes, Uni-μFCM substantially expanded the phenotypic space accessible to single-cell applications in leukemia gating, tumor classification, and hierarchy determination of cancer stem cells.

Single cell quantification of microRNA from small numbers of non-invasively sampled primary human cells

Expression levels of microRNAs (miRNAs) in single cells are low and conventional miRNA detection methods require amplification that can be complex, time-consuming, costly and may bias results. Single cell microfluidic platforms have been developed; however, current approaches are unable to absolutely quantify single miRNA molecules expressed in single cells. Herein, we present an amplification-free sandwich hybridisation assay to detect single miRNA molecules in single cells using a microfluidic platform that optically traps and lyses individual cells. Absolute quantification of miR-21 and miR-34a molecules was achieved at a single cell level in human cell lines and validated using real-time qPCR. The sensitivity of the assay was demonstrated by quantifying single miRNA molecules in nasal epithelial cells and CD3⁺ T-cells, as well as nasal fluid collected non-invasively from healthy individuals. This platform requires ~50 cells or ~30 µL biofluid and can be extended for other miRNA targets therefore it could monitor miRNA levels in disease progression or clinical studies.

Development of constrictional microchannels and the recurrent neural network in single-cell protein analysis

Introduction: As the golden approach of single-cell analysis, fluorescent flow cytometry can estimate single-cell proteins with high throughputs, which, however, cannot translate fluorescent intensities into protein numbers. Methods: This study reported a fluorescent flow cytometry based on constrictional microchannels for quantitative measurements of single-cell fluorescent levels and the recurrent neural network for data analysis of fluorescent profiles for high-accuracy cell-type classification. Results: As a demonstration, fluorescent profiles (e.g., FITC labeled β-actin antibody, PE labeled EpCAM antibody and PerCP labeled β-tubulin antibody) of individual A549 and CAL 27 cells were firstly measured and translated into protein numbers of 0.56 ± 0.43 × 10⁴, 1.78 ± 1.0⁶ × 10⁶ and 8.11 ± 4.89 × 10⁴ of A549 cells (n_cell = 10232), and 3.47 ± 2.45 × 10⁴, 2.65 ± 1.19 × 10⁶ and 8.61 ± 5.25 × 10⁴ of CAL 27 cells (n_cell = 16376) based on the equivalent model of the constrictional microchannel. Then, the feedforward neural network was used to process these single-cell protein expressions, producing a classification accuracy of 92.0% for A549 vs. CAL 27 cells. In order to further increase the classification accuracies, as a key subtype of the recurrent neural network, the long short-term memory (LSTM) neural network was adopted to process fluorescent pulses sampled in constrictional microchannels directly, producing a classification accuracy of 95.5% for A549 vs. CAL 27 cells after optimization. Discussion: This fluorescent flow cytometry based on constrictional microchannels and recurrent neural network can function as an enabling tool of single-cell analysis and contribute to the development of quantitative cell biology.

Demands and technical developments of clinical flow cytometry with emphasis in quantitative, spectral, and imaging capabilities

As the gold-standard method for single-cell analysis, flow cytometry enables high-throughput and multiple-parameter characterization of individual biological cells. This review highlights the demands for clinical flow cytometry in laboratory hematology (e.g., diagnoses of minimal residual disease and various types of leukemia), summarizes state-of-the-art clinical flow cytometers (e.g., FACSLyricTM by Becton Dickinson, DxFLEX by Beckman Coulter), then considers innovative technical improvements in flow cytometry (including quantitative, spectral, and imaging approaches) to address the limitations of clinical flow cytometry in hematology diagnosis. Finally, driven by these clinical demands, future developments in clinical flow cytometry are suggested.

Quantitative flow cytometry leveraging droplet‐based constriction microchannels with high reliability and high sensitivity

This study presented a quantitative flow cytometry leveraging droplet-based constriction microchannels with high reliability and high sensitivity. Droplets encapsulating single cells and even distribution of fluorescein labeled antibodies removed from targeted cells deformed through the constriction microchannel where the excited fluorescent signals were sampled and interpreted into numbers of proteins based on volume equivalence in measurement of droplets and calibration of fluorescence. To improve the detection reliability, a comprehensive analysis and comparison of multiple stripping agents such as proteinase K, guanidine hydrochloride, and urea was conducted. To improve the detection sensitivity, light modulation was used to address electrical noises and quartz microchannels were fabricated to address optical noises. As a demonstration, based on this quantitative flow cytometry of droplet microfluidics, (1) mutant p53 expressions of single cells were quantified as 1.95 ± 0.60 × 10⁵ (n_cell  = 2918 of A431) and 1.30 ± 0.70 × 10⁵ (n_cell  = 3954 of T47D); (2) single-cell expressions of Ras, c-Myc, and β-tubulin were quantified as 1.90 ± 0.59 × 10⁵ , 4.39 ± 1.44 × 10⁵ , and 2.97 ± 0.81 × 10⁵ (n_cell  = 3298 of CAL 27), 1.83 ± 0.58 × 10⁵ , 2.08 ± 0.13 × 10⁶ , and 1.96 ± 0.74 × 10⁵ (n_cell  = 5459 of WSU-HN6). As a microfluidic tool capable of quantitatively estimating single-cell protein expressions, this methodology may provide a new quantitative perspective for the field of flow cytometry.

Highly sensitive detection of multiple proteins from single cells by MoS2-FET biosensors

Single-cell analysis of proteins is critical to gain precise information regarding the mechanisms that dictate the heterogeneity in cellular phenotypes and their differential response to internal and external stimuli. However, tools that allow sensitive and easy measurement of proteins in individual cells are still limited. The emerging semiconductor-based bioelectronics may provide a new approach to overcome the challenges in this field, however its utility in single-cell protein analysis has not been explored. In this study, we investigated multiple protein detection in single cells by MoS₂ field effect transistors (MoS₂-FETs) modified with specific biological probes. First, β-actin antibody was connected to the surface of MoS₂-FETs by covalent bonds, and the fabricated device was tested using β-actin solution with concentrations from 10^-9 to 10^-3 μg/μL. Next, we examined the application of MoS₂-FET for protein analysis in complex biological samples, and the device showed electrical signal response to human embryonic kidney cell line HEK293T in a dose-dependent manner. Furthermore, we applied this method to analyze individual liver cancer MHCC-97L cells, targeting four cellular proteins, including β-actin, epidermal growth factor receptor, sirtuin-2, and glyceraldehyde-3-phosphate dehydrogenase. The devices modified with corresponding probes could identify the target proteins and showed cell number-dependent responses. As a proof of principle, we demonstrated sensitive and multiplexed detection of proteins in single cells using MoS₂-FETs. The biosensor and this detection method are cost-efficient and user-friendly with broad application prospects in biological studies and clinical diagnosis.

Application of Single-Cell Multi-Omics in Dissecting Cancer Cell Plasticity and Tumor Heterogeneity

Tumor heterogeneity, a hallmark of cancer, impairs the efficacy of cancer therapy and drives tumor progression. Exploring inter- and intra-tumoral heterogeneity not only provides insights into tumor development and progression, but also guides the design of personalized therapies. Previously, high-throughput sequencing techniques have been used to investigate the heterogeneity of tumor ecosystems. However, they could not provide a high-resolution landscape of cellular components in tumor ecosystem. Recently, advance in single-cell technologies has provided an unprecedented resolution to uncover the intra-tumoral heterogeneity by profiling the transcriptomes, genomes, proteomes and epigenomes of the cellular components and also their spatial distribution, which greatly accelerated the process of basic and translational cancer research. Importantly, it has been demonstrated that some cancer cells are able to transit between different states in order to adapt to the changing tumor microenvironment, which led to increased cellular plasticity and tumor heterogeneity. Understanding the molecular mechanisms driving cancer cell plasticity is critical for developing precision therapies. In this review, we summarize the recent progress in dissecting the cancer cell plasticity and tumor heterogeneity by use of single-cell multi-omics techniques.

Microfluidics-Based Sensing of Biospecies

Over the past decades, microfluidic devices based on many advanced techniques have aroused widespread attention in the fields of chemical, biological, and analytical applications. Integration of microdevices with a variety of chip designs will facilitate promising functionality. Notably, the combination of microfluidics with functional nanomaterials may provide creative ideas to achieve rapid and sensitive detection of various biospecies. In this review, focused on the microfluids and microdevices in terms of their fabrication, integration, and functions, we summarize the up-to-date developments in microfluidics-based analysis of biospecies, where biomarkers, small molecules, cells, and pathogens as representative biospecies have been explored in-depth. The promising applications of microfluidic biosensors including clinical diagnosis, food safety control, and environmental monitoring are also discussed. This review aims to highlight the importance of microfluidics-based biosensors in achieving high throughput, highly sensitive, and low-cost analysis and to promote microfluidics toward a wider range of applications.

Advances of Single-Cell Protein Analysis

Proteins play a significant role in the key activities of cells. Single-cell protein analysis provides crucial insights in studying cellular heterogeneities. However, the low abundance and enormous complexity of the proteome posit challenges in analyzing protein expressions at the single-cell level. This review summarizes recent advances of various approaches to single-cell protein analysis. We begin by discussing conventional characterization approaches, including fluorescence flow cytometry, mass cytometry, enzyme-linked immunospot assay, and capillary electrophoresis. We then detail the landmark advances of microfluidic approaches for analyzing single-cell protein expressions, including microfluidic fluorescent flow cytometry, droplet-based microfluidics, microwell-based assay (microengraving), microchamber-based assay (barcoding microchips), and single-cell Western blotting, among which the advantages and limitations are compared. Looking forward, we discuss future research opportunities and challenges for multiplexity, analyte, throughput, and sensitivity of the microfluidic approaches, which we believe will prompt the research of single-cell proteins such as the molecular mechanism of cell biology, as well as the clinical applications for tumor treatment and drug development.