Label-free Single-cell Analysis Research Articles

From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis.

Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.

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.

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.

Nanosensor Chemical Cytometry: Advances and Opportunities in Cellular Therapy and Precision Medicine.

With the definition of therapeutics now encompassing transplanted or engineered cells and their molecular products, there is a growing scientific necessity for analytics to understand this new category of drugs. This Perspective highlights the recent development of new measurement science on label-free single cell analysis, nanosensor chemical cytometry (NCC), and their potential for cellular therapeutics and precision medicine. NCC is based on microfluidics integrated with fluorescent nanosensor arrays utilizing the optical lensing effect of a single cell to real-time extract molecular properties and correlate them with physical attributes of single cells. This new class of cytometry can quantify the heterogeneity of the multivariate physicochemical attributes of the cell populations in a completely label-free and nondestructive way and, thus, suggest the vein-to-vein conditions for the safe therapeutic applications. After the introduction of the NCC technology, we suggest the technological development roadmap for the maturation of the new field: from the sensor/chip design perspective to the system/software development level based on hardware automation and deep learning data analytics. The advancement of this new single cell sensing technology is anticipated to aid rich and multivariate single cell data setting and support safe and reliable cellular therapeutics. This new measurement science can lead to data-driven personalized precision medicine.

Label-free microfluidic cell sorting and detection for rapid blood analysis.

Blood tests are considered as standard clinical procedures to screen for markers of diseases and health conditions. However, the complex cellular background (>99.9% RBCs) and biomolecular composition often pose significant technical challenges for accurate blood analysis. An emerging approach for point-of-care blood diagnostics is utilizing "label-free" microfluidic technologies that rely on intrinsic cell properties for blood fractionation and disease detection without any antibody binding. A growing body of clinical evidence has also reported that cellular dysfunction and their biophysical phenotypes are complementary to standard hematoanalyzer analysis (complete blood count) and can provide a more comprehensive health profiling. In this review, we will summarize recent advances in microfluidic label-free separation of different blood cell components including circulating tumor cells, leukocytes, platelets and nanoscale extracellular vesicles. Label-free single cell analysis of intrinsic cell morphology, spectrochemical properties, dielectric parameters and biophysical characteristics as novel blood-based biomarkers will also be presented. Next, we will highlight research efforts that combine label-free microfluidics with machine learning approaches to enhance detection sensitivity and specificity in clinical studies, as well as innovative microfluidic solutions which are capable of fully integrated and label-free blood cell sorting and analysis. Lastly, we will envisage the current challenges and future outlook of label-free microfluidics platforms for high throughput multi-dimensional blood cell analysis to identify non-traditional circulating biomarkers for clinical diagnostics.

A Systematic Study of Size Correlation and Young's Modulus Sensitivity for Cellular Mechanical Phenotyping by Microfluidic Approaches (Adv. Healthcare Mater. 19/2022)

Cellular Mechanical Phenotyping Cellular mechanical properties are a class of intrinsic biophysical markers for cell state and health. In article 2200628, Ye Ai and co-workers perform a systematic study to compare two types of microfluidic cellular mechanical phenotyping techniques, contactless hydro-stretching deformability cytometry, and contact constriction deformability cytometry, which provides guidance to choose the most suitable method for label-free single cell analysis applications.

Neuroblastoma Cells Classification Through Learning Approaches by Direct Analysis of Digital Holograms

The label-free single cell analysis by machine and Deep Learning, in combination with digital holography in transmission microscope configuration, is becoming a powerful framework exploited for phenotyping biological samples. Usually, quantitative phase images of cells are retrieved from the reconstructed complex diffraction patterns and used as inputs of a deep neural network. However, the phase retrieval process can be very time consuming and prone to errors. Here we address the classification of cells by using learning strategies with images coming directly from the raw recorded digital holograms, i.e. without any data processing or refocusing involved. Indeed, in the raw digital hologram the entire complex amplitude information of the sample is intrinsically embedded in the form of modulated fringes. We develop a training strategy, based on deep and feature based machine learning models, in order extract such information by skipping the classical reconstruction process for classifying different neuroblastoma cells. We provided an experimental validation by using the proposed strategy to classify two neuroblastoma cell lines.

High-Content Label-Free Single-Cell Analysis with a Microfluidic Device Using Programmable Scanning Electrochemical Microscopy.

The cellular heterogeneity and plasticity are often overlooked due to the averaged bulk assay in conventional methods. Optical imaging-based single-cell analysis usually requires specific labeling of target molecules inside or on the surface of the cell membrane, interfering with the physiological homeostasis of the cell. Scanning electrochemical microscopy (SECM), as an alternative approach, enables label-free imaging of single cells, which still confronts the challenge that the long-time scanning process is not feasible for large-scale analysis at the single-cell level. Herein, we developed a methodology combining a programmable SECM (P-SECM) with an addressable microwell array, which dramatically shortened the time consumption for the topography detection of the micropits array occupied by the polystyrene beads as well as the evaluation of alkaline phosphatase (ALP) activity of the 82 single cells compared with the traditional SECM imaging. This new arithmetic was based on the line scanning approach, enabling analysis of over 900 microwells within 1.2 h, which is 10 times faster than conventional SECM imaging. By implementing this configuration with the dual-mediator-based voltage-switching (VSM) mode, we investigated the activity of ALP, a promising marker for cancer stem cells, in hundreds of tumor and stromal cells on a single microwell device. The results discovered that not only a higher ALP activity is presented in cancer cells but also the heterogeneous distribution of kinetic constant (kf value) of ALP activity can be obtained at the single-cell level. By directly relating large numbers of addressed cells on the scalable microfluidic device to the deterministic routing of the above SECM tip, our platform holds potential as a high-content screening tool for label-free single-cell analysis.

Adaptive dynamic range shift (ADRIFT) quantitative phase imaging

Quantitative phase imaging (QPI) with its high-contrast images of optical phase delay (OPD) maps is often used for label-free single-cell analysis. Contrary to other imaging methods, sensitivity improvement has not been intensively explored because conventional QPI is sensitive enough to observe the surface roughness of a substrate that restricts the minimum measurable OPD. However, emerging QPI techniques that utilize, for example, differential image analysis of consecutive temporal frames, such as mid-infrared photothermal QPI, mitigate the minimum OPD limit by decoupling the static OPD contribution and allow measurement of much smaller OPDs. Here, we propose and demonstrate supersensitive QPI with an expanded dynamic range. It is enabled by adaptive dynamic range shift through a combination of wavefront shaping and dark-field QPI techniques. As a proof-of-concept demonstration, we show dynamic range expansion (sensitivity improvement) of QPI by a factor of 6.6 and its utility in improving the sensitivity of mid-infrared photothermal QPI. This technique can also be applied for wide-field scattering imaging of dynamically changing nanoscale objects inside and outside a biological cell without losing global cellular morphological image information.

Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential

Identification of the metastatic potential represents one of the most important tasks for molecular imaging of cancer. While molecular imaging of metastases has witnessed substantial progress as an area of clinical inquiry, determining precisely what differentiates the metastatic phenotype has proven to be more elusive. In this study, we utilize both the morphological and molecular information provided by 3D optical diffraction tomography and Raman spectroscopy, respectively, to propose a label-free route for optical phenotyping of cancer cells at single-cell resolution. By using an isogenic panel of cell lines derived from MDA-MB-231 breast cancer cells that vary in their metastatic potential, we show that 3D refractive index tomograms can capture subtle morphological differences among the parental, circulating tumor cells, and lung metastatic cells. By leveraging its molecular specificity, we demonstrate that coarse Raman microscopy is capable of rapidly mapping a sufficient number of cells for training a random forest classifier that can accurately predict the metastatic potential of cells at a single-cell level. We also perform multivariate curve resolution alternating least squares decomposition of the spectral dataset to demarcate spectra from cytoplasm and nucleus, and test the feasibility of identifying metastatic phenotypes using the spectra only from the cytoplasmic and nuclear regions. Overall, our study provides a rationale for employing coarse Raman mapping to substantially reduce measurement time thereby enabling the acquisition of reasonably large training datasets that hold the key for label-free single-cell analysis and, consequently, for differentiation of indolent from aggressive phenotypes.

Large-scale label-free single-cell analysis of paramylon in Euglena gracilis by high-throughput broadband Raman flow cytometry.

Microalga-based biomaterial production has attracted attention as a new source of drugs, foods, and biofuels. For enhancing the production efficiency, it is essential to understand its differences between heterogeneous microalgal subpopulations. However, existing techniques are not adequate to address the need due to the lack of single-cell resolution or the inability to perform large-scale analysis and detect small molecules. Here we demonstrated large-scale single-cell analysis of Euglena gracilis (a unicellular microalgal species that produces paramylon as a potential drug for HIV and colon cancer) with our recently developed high-throughput broadband Raman flow cytometer at a throughput of >1,000 cells/s. Specifically, we characterized the intracellular content of paramylon from single-cell Raman spectra of 10,000 E. gracilis cells cultured under five different conditions and found that paramylon contents in E. gracilis cells cultured in an identical condition is given by a log-normal distribution, which is a good model for describing the number of chemicals in a reaction network. The capability of characterizing distribution functions in a label-free manner is an important basis for isolating specific cell populations for synthetic biology via directed evolution based on the intracellular content of metabolites.

Multiplex Stimulated Raman Scattering Imaging Cytometry Reveals Lipid-Rich Protrusions in Cancer Cells under Stress Condition.

SummaryIn situ measurement of cellular metabolites is still a challenge in biology. Conventional methods, such as mass spectrometry or fluorescence microscopy, would either destroy the sample or introduce strong perturbations to target molecules. Here, we present multiplex stimulated Raman scattering (SRS) imaging cytometry as a label-free single-cell analysis platform with chemical specificity and high-throughput capabilities. Using SRS imaging cytometry, we studied the metabolic responses of human pancreatic cancer cells under stress by starvation and chemotherapeutic drug treatments. We unveiled protrusions containing lipid droplets as a metabolic marker for stress-resistant cancer cells. Furthermore, by spectroscopic SRS mapping, we unveiled that triglyceride in lipid droplets are used for local energy production through lipolysis, autophagy, and β-oxidation. Our findings demonstrate the potential of targeting lipid metabolism for selective treatment of stress-resistant cancers. Collectively, these results highlight SRS imaging cytometry as a powerful label-free tool for biological discoveries with a high-throughput, high-content capacity.

Using flow cytometry and multistage machine learning to discover label-free signatures of algal lipid accumulation

Most applications of flow cytometry or cell sorting rely on the conjugation of fluorescent dyes to specific biomarkers. However, labeled biomarkers are not always available, they can be costly, and they may disrupt natural cell behavior. Label-free quantification based upon machine learning approaches could help correct these issues, but label replacement strategies can be very difficult to discover when applied labels or other modifications in measurements inadvertently modify intrinsic cell properties. Here we demonstrate a new, but simple approach based upon feature selection and linear regression analyses to integrate statistical information collected from both labeled and unlabeled cell populations and to identify models for accurate label-free single-cell quantification. We verify the method’s accuracy to predict lipid content in algal cells (Picochlorum soloecismus) during a nitrogen starvation and lipid accumulation time course. Our general approach is expected to improve label-free single-cell analysis for other organisms or pathways, where biomarkers are inconvenient, expensive, or disruptive to downstream cellular processes.

Biophysical phenotyping of single cells using a differential multiconstriction microfluidic device with self-aligned 3D electrodes

Precise measurement of mechanical and electrical properties of single cells can yield useful information on the physiological and pathological state of cells. In this work, we develop a differential multiconstriction microfluidic device with self-aligned 3D electrodes to simultaneously characterize the deformability, electrical impedance and relaxation index of single cells at a high throughput manner (>430 cell/min). Cells are pressure-driven to flow through a series of sequential microfluidic constrictions, during which deformability, electrical impedance and relaxation index of single cells are extracted simultaneously from impedance spectroscopy measurements. Mechanical and electrical phenotyping of untreated, Cytochalasin B treated and N-Ethylmaleimide treated MCF-7 breast cancer cells demonstrate the ability of our system to distinguish different cell populations purely based on these biophysical properties. In addition, we quantify the classification of different cell types using a back propagation neural network. The trained neural network yields the classification accuracy of 87.8% (electrical impedance), 70.1% (deformability), 42.7% (relaxation index) and 93.3% (combination of electrical impedance, deformability and relaxation index) with high sensitivity (93.3%) and specificity (93.3%) for the test group. Furthermore, we have demonstrated the cell classification of a cell mixture using the presented biophysical phenotyping technique with the trained neural network, which is in quantitative agreement with the flow cytometric analysis using fluorescent labels. The developed concurrent electrical and mechanical phenotyping provide great potential for high-throughput and label-free single cell analysis.

High-throughput label-free molecular fingerprinting flow cytometry.

Flow cytometry is an indispensable tool in biology for counting and analyzing single cells in large heterogeneous populations. However, it predominantly relies on fluorescent labeling to differentiate cells and, hence, comes with several fundamental drawbacks. Here, we present a high-throughput Raman flow cytometer on a microfluidic chip that chemically probes single live cells in a label-free manner. It is based on a rapid-scan Fourier-transform coherent anti-Stokes Raman scattering spectrometer as an optical interrogator, enabling us to obtain the broadband molecular vibrational spectrum of every single cell in the fingerprint region (400 to 1600 cm-1) with a record-high throughput of ~2000 events/s. As a practical application of the method not feasible with conventional flow cytometry, we demonstrate high-throughput label-free single-cell analysis of the astaxanthin productivity and photosynthetic dynamics of Haematococcus lacustris.

Oscillator-Based Reactance Sensors With Injection Locking for High-Throughput Flow Cytometry Using Microwave Dielectric Spectroscopy

This paper presents the analysis and design of oscillator-based reactance sensors employing injection locking for high-throughput label-free single-cell analysis using dielectric spectroscopy at microwave frequencies. By injection-locking two sensing LC-oscillators with an I/Q excitation source, the measurement of the sample-induced frequency shift caused by the interaction with the electromagnetic fields is performed through phase detection with injection-strength-dependent transducer gain. Such inherent phase amplification offered by the injection locking not only relaxes the design requirement for the readout circuits but also maintains the highest rejection against common-mode errors associated with the drift of the supply voltage and the environmental parameters. To reduce flicker noise contribution, a chopping technique employing phase modulation is exploited. In addition, this paper presents a novel ping-pong chopping approach to alleviate chopping-induced dc offset. In this prototype, four sensing channels, covering frequencies between 6.5 and 30 GHz, are distributed along a microfluidic channel fabricated with standard photolithography. Measurements show that the proposed microwave capacitive sensors achieve a ${sub}{- }{a}{{F}_{{rms}}}$ of noise sensitivity at 100 kHz filtering bandwidth, enabling measurement throughput exceeding 1 k cells/s. The sensor prototype is implemented in 65 nm CMOS technology and consumes 65 mW at 1 V supply.

2D light scattering static cytometry for label-free single cell analysis with submicron resolution.

Conventional optical cytometric techniques usually measure fluorescence or scattering signals at fixed angles from flowing cells in a liquid stream. Here we develop a novel cytometer that employs a scanning optical fiber to illuminate single static cells on a glass slide, which requires neither microfluidic fabrication nor flow control. This static cytometric technique measures two dimensional (2D) light scattering patterns via a small numerical aperture (0.25) microscope objective for label-free single cell analysis. Good agreement is obtained between the yeast cell experimental and Mie theory simulated patterns. It is demonstrated that the static cytometer with a microscope objective of a low resolution around 1.30 μm has the potential to perform high resolution analysis on yeast cells with distributed sizes. The capability of the static cytometer for size determination with submicron resolution is validated via measurements on standard microspheres with mean diameters of 3.87 and 4.19 μm. Our 2D light scattering static cytometric technique may provide an easy-to-use, label-free, and flow-free method for single cell diagnostics.

Reliable Cell Segmentation Based on Spectral Phasor Analysis of Hyperspectral Stimulated Raman Scattering Imaging Data

Hyperspectral stimulated Raman scattering (SRS) imaging has rapidly become an emerging tool for high content analyses of cell and tissue systems. The label-free nature of SRS imaging combined with its chemical specificity allows in situ and in vivo biochemical quantification at submicrometer resolution without sectioning and staining. Current hyperspectral SRS data analysis methods are based on either linear unmixing or multivariate analysis, which are not sensitive to small spectral variations and often provide obscure information on the cell composition. Here, we demonstrate a spectral phasor analysis method that allows fast and reliable cellular organelle segmentation of mammalian cells, without any a priori knowledge of their composition or basis spectra. We further show that, in combination with a branch-bound algorithm for optimal selection of a few wavenumbers, spectral phasor analysis provides a robust solution to label-free single cell analysis.