Label-free Cell Research Articles

Label-free image-encoded microfluidic cell sorter with a scanning Bessel beam.

The microfluidic-based, label-free image-guided cell sorter offers a low-cost, high information content, and disposable solution that overcomes many limitations in conventional cell sorters. However, flow confinement for most microfluidic devices is generally only one-dimensional using sheath flow. As a result, the equilibrium distribution of cells spreads beyond the focal plane of commonly used Gaussian laser excitation beams, resulting in a large number of blurred images that hinder subsequent cell sorting based on cell image features. To address this issue, we present a Bessel–Gaussian beam image-guided cell sorter with an ultra-long depth of focus, enabling focused images of >85% of passing cells. This system features label-free sorting capabilities based on features extracted from the output temporal waveform of a photomultiplier tube (PMT) detector. For the sorting of polystyrene beads, SKNO1 leukemia cells, and Scenedesmus green algae, our results indicate a sorting purity of 97%, 97%, and 98%, respectively, showing that the temporal waveforms from the PMT outputs have strong correlations with cell image features. These correlations are also confirmed by off-line reconstructed cell images from a temporal–spatial transformation algorithm tailored to the scanning Bessel–Gaussian beam.

Biofilm inhibition by biocompatible poly(ε-caprolactone) nanocapsules loaded with essential oils and their cyto/genotoxicity to human keratinocyte cell line

Essential oils (EOs) of Thymus capitatus (Th) carvacrol chemotype and Origanum vulgare (Or) thymol and carvacrol chemotype were encapsulated in biocompatible poly(ε-caprolactone) nanocapsules (NCs). These nanosystems exhibited antibacterial, antifungal, and antibiofilm activities against Staphylococcus aureus, Escherichia coli, and Candida albicans. Th-NCs and Or-NCs were more effective against all tested strains than pure EOs and at the same time were not cytotoxic on HaCaT (T0020001) human keratinocyte cell line. The genotoxic effects of EO-NCs and EOs on HaCaT were evaluated using an alkaline comet assay for the first time, revealing that Th-NCs and Or-NCs did not induce DNA damage compared with untreated control HaCaT cells in vitro after 24 h. The cells morphological changes were assessed by label-free live cell Raman imaging. This study demonstrate the ability of poly(ε-caprolactone) nanocapsules loaded with thyme and oregano EOs to reduce microbial and biofilm growth and could be an ecological alternative in the development of new antimicrobial strategies.

Manipulating Osteogenic/Adipogenic Balance in Mesenchymal Stromal Cells (MSCs) for Hematopoietic Applications

Aims: Bone marrow-derived mesenchymal stromal cells (BM-MSCs) have the potential to improve hematopoietic transplantation by supporting hematopoietic stem and progenitor cell (HSPC) expansion as well as by promoting HSPC engraftment to the BM. However, culture expanded MSCs becomes a mixture of progenitor cells committed to different cell lineages, each with different potencies for any one desired application. Clinically, the accumulation of adipogenic MSCs in the BM is believed to contribute towards age-related impairment of regeneration and hematopoiesis; this observation, together with our understanding of the inverse relationship between MSC osteogenic and adipogenic differentiation prompted a more in depth study of the ability of these different MSC lineages to support hematopoietic clinical applications. Clinically relevant methods for engineering a pure cell population from heterogeneous MSC culture will also be discussed.Methods: MSCs were derived from the healthy BM of patients aged 30-50 years. At various stages of osteogenic and adipogenic differentiation in vitro , cultures were evaluated for their ability to support cord blood (CB) CD34+ HSC expansion and stimulate regenerative processes such as angiogenesis and cell proliferation. qPCR, coupled with secretome analysis was performed to discover gene expression markers for MSCs with potent hematopoietic supportive capabilities. Finally, due to the lack of unique surface markers for identification of MSC subpopulations, we utilized multivariate biophysical analysis (microfluidic sorting, atomic force microscopy, etc.), correlating these quantitative measures with biomolecular markers as well as in vitro and in vivo functionality. Novel label-free cell separation strategies that take advantage of unique biophysical traits in a potent MSC subpopulation for HSPC transplantation was also developed to enable clinical translation.Results: Differentiated osteoblasts (OB) as well as pre-OB demonstrated a higher ability to support CB CD34+ HSC expansion compared to undifferentiated MSCs or MSCs in the adipogenic state. Over a period of 10 days, the total HSPC fold expansion was 44.4 ± 4.83, 57.8 ± 10.67, 40.2 ± 4.65, 34.2 ± 6.18 and 13.8 ± 5.50 for OB, pre-OB, MSCs, pre-AD and AD, respectively (n = 5). Absolute expanded CD34+ HSCs was highest with pre-OB cultures (~1.7 fold, n = 5) compared to undifferentiated MSCs. Adipogenic MSCs had much reduced capabilities for supporting HSPC expansion. In vitro angiogenic and proliferation assays showed the greatest degree of HUVEC sprouting and proliferation (~ 2.6 fold greater than undifferentiated MSCs) when exposed to a pre-OB secretome. Pre-OBs are distinct in their transcriptome (up-regulated osteopontin, runx2, angiopoietin-1, FGF1, etc) but no surface marker reliably distinguishes them from other MSCs. We subsequently found a minimal set of three biophysical markers: cell diameter, membrane stiffness and nuclear membrane fluctuations, which collectively are predictive of MSC subpopulations (multipotent vs committed progenitors). We hypothesized and experimentally verified that a biophysically derived pre-OB subpopulation, defined biophysically as MSCs with >20 μm diameter, >375Pa stiffness and <1.2 % nuclear fluctuation, exhibits significantly greater efficacy over other MSC subpopulations for promoting bone marrow repair (~2-3 fold improvements in medial survival following BM injury) and expansion of HSPCs (~1.5-2 fold increase in HSPC numbers). Following co-transplantation with HSPCs, biophysically derived pre-OB promoted faster engraftment of donor cells in the peripheral blood by week 4 (~ 9% vs < 5% in other MSC groups, P<0.05).Conclusion: These results demonstrate a correlation between MSCs with high osteogenic activity and the capabilities for BM repair as well as hematopoietic support, making such cells ideal for augmentation of HSC transplantations. In the absence of reliable surface markers for isolation of these pre-OBs, we developed a clinically relevant biophysical isolation strategy for derivation of pre-OBs that can markedly improve the efficacy of MSC-based approaches for augmentation of hematopoietic transplants. DisclosuresNo relevant conflicts of interest to declare.

An easy-fabricated and disposable polymer-film microfluidic impedance cytometer for cell sensing

We report here an easy-fabricated and disposable polymer-film microfluidic impedance cytometer (PMIC) integrated with inertial focusing and parallel facing electrodes for cell sensing. The cells are first focused in an asymmetric serpentine channel, and then their impedance signals are measured when passing through the electrode region. The proposed PMIC device is the first impedance cytometer that is fabricated into a flexible sheet (with a thickness of 0.45 mm) by using the materials of commonly-available ITO-coated polymer films and double-sided adhesive tapes, the whole fabrication process is shortened from traditional 3–4 days to less than 5 min by using UV laser cutting. To verify the feasibility of our device for cell sensing, we explore the focusing behaviors of three differently sized particles and two types of tumor cells, and analyze their impedance signals. The results show that our device is capable of obtaining impedance information on numbers, diameters, and longitudinal positions of cells. We envision that our PMIC device is promising in label-free cell sensing owning to the advantages of low cost, small footprint, and simple fabrication.

Spectral analysis of photo-induced delayed luminescence from mesenchymal stem cells for label-free cell viability assessment

Spectral analysis of photo-induced delayed luminescence from mesenchymal stem cells for label-free cell viability assessment

Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma.

Deep learning has emerged as the technique of choice for identifying hidden patterns in cell imaging data but is often criticized as "black box." Here, we employ a generative neural network in combination with supervised machine learning to classify patient-derived melanoma xenografts as "efficient" or "inefficient" metastatic, validate predictions regarding melanoma cell lines with unknown metastatic efficiency in mouse xenografts, and use the network to generate in silico cell images that amplify the critical predictive cell properties. These exaggerated images unveiled pseudopodial extensions and increased light scattering as hallmark properties of metastatic cells. We validated this interpretation using live cells spontaneously transitioning between states indicative of low and high metastatic efficiency. This study illustrates how the application of artificial intelligence can support the identification of cellular properties that are predictive of complex phenotypes and integrated cell functions but are too subtle to be identified in the raw imagery by a human expert. A record of this paper's transparent peer review process is included in the supplemental information. VIDEO ABSTRACT.

Integration of light scattering with machine learning for label free cell detection.

Light scattering has been used for label-free cell detection. The angular light scattering patterns from the cells are unique to them based on the cell size, nucleus size, number of mitochondria, and cell surface roughness. The patterns collected from the cells can then be classified based on different image characteristics. We have also developed a machine learning (ML) method to classify these cell light scattering patterns. As a case study we have used this light scattering technique integrated with the machine learning to analyze staurosporine-treated SH-SY5Y neuroblastoma cells and compare them to non-treated control cells. Experimental results show that the ML technique can provide a classification accuracy (treated versus non-treated) of over 90%. The predicted percentage of the treated cells in a mixed solution is within 5% of the reference (ground-truth) value and the technique has the potential to be a viable method for real-time detection and diagnosis.

Non-invasive cell classification using the Paint Raman Express Spectroscopy System (PRESS)

Raman scattering represents the distribution and abundance of intracellular molecules, including proteins and lipids, facilitating distinction between cellular states non-invasively and without staining. However, the scattered light obtained from cells is faint and cells have complex structures, making it difficult to obtain a Raman spectrum covering the entire cell in a short time using conventional methods. This also prevents efficient label-free cell classification. In the present study, we developed the Paint Raman Express Spectroscopy System, which uses two fast-rotating galvano mirrors to obtain spectra from a wide area of a cell. By using this system and applying machine learning, we were able to acquire broad spectra of a variety of human and mouse cell types, including pluripotent stem cells and confirmed that each cell type can be classified with high accuracy. Moreover, we classified different activation states of human T cells, despite their similar morphology. This system could be used for rapid and low-cost drug evaluation and quality management for drug screening in cell-based assays.

Light scattering pattern specific convolutional network static cytometry for label-free classification of cervical cells.

Cervical cancer is a major gynecological malignant tumor that threatens women's health. Current cytological methods have certain limitations for cervical cancer early screening. Light scattering patterns can reflect small differences in the internal structure of cells. In this study, we develop a light scattering pattern specific convolutional network (LSPS-net) based on deep learning algorithm and integrate it into a 2D light scattering static cytometry for automatic, label-free analysis of single cervical cells. An accuracy rate of 95.46% for the classification of normal cervical cells and cancerous ones (mixed C-33A and CaSki cells) is obtained. When applied for the subtyping of label-free cervical cell lines, we obtain an accuracy rate of 93.31% with our LSPS-net cytometric technique. Furthermore, the three-way classification of the above different types of cells has an overall accuracy rate of 90.90%, and comparisons with other feature descriptors and classification algorithms show the superiority of deep learning for automatic feature extraction. The LSPS-net static cytometry may potentially be used for cervical cancer early screening, which is rapid, automatic and label-free.

Multiscale Assay of Unlabeled Neurite Dynamics Using Phase Imaging with Computational Specificity.

Primary neuronal cultures have been widely used to study neuronal morphology, neurophysiology, neurodegenerative processes, and molecular mechanism of synaptic plasticity underlying learning and memory. However, the unique behavioral properties of neurons make them challenging to study, with phenotypic differences expressed as subtle changes in neuronal arborization rather than easy-to-assay features such as cell count. The need to analyze morphology, growth, and intracellular transport has motivated the development of increasingly sophisticated microscopes and image analysis techniques. Due to its high-contrast, high-specificity output, many assays rely on confocal fluorescence microscopy, genetic methods, or antibody staining techniques. These approaches often limit the ability to measure quantitatively dynamic activity such as intracellular transport and growth. In this work, we describe a method for label-free live-cell cell imaging with antibody staining specificity by estimating the associated fluorescence signals via quantitative phase imaging and deep convolutional neural networks. This computationally inferred fluorescence image is then used to generate a semantic segmentation map, annotating subcellular compartments of live unlabeled neural cultures. These synthetic fluorescence maps were further applied to study the time-lapse development of hippocampal neurons, highlighting the relationships between the cellular dry mass production and the dynamic transport activity within the nucleus and neurites. Our implementation provides a high-throughput strategy to analyze neural network arborization dynamically, with high specificity and without the typical phototoxicity and photobleaching limitations associated with fluorescent markers.

The Xenopus spindle is as dense as the surrounding cytoplasm.

The mitotic spindle is a self-organizing molecular machine, where hundreds of different molecules continuously interact to maintain a dynamic steady state. While our understanding of key molecular players in spindle assembly is significant, it is still largely unknown how the spindle's material properties emerge from molecular interactions. Here, we use correlative fluorescence imaging and label-free three-dimensional optical diffraction tomography (ODT) to measure the Xenopus spindle's mass density distribution. While the spindle has been commonly referred to as a denser phase of the cytoplasm, we find that it has the same density as its surrounding, which makes it neutrally buoyant. Molecular perturbations suggest that spindle mass density can be modulated by tuning microtubule nucleation and dynamics. Together, ODT provides direct, unbiased, and quantitative information of the spindle's emergent physical properties-essential to advance predictive frameworks of spindle assembly and function.

Disease-Relevant Single Cell Photonic Signatures Identify S100\u03b2 Stem Cells and their Myogenic Progeny in Vascular Lesions

A hallmark of subclinical atherosclerosis is the accumulation of vascular smooth muscle cell (SMC)-like cells leading to intimal thickening and lesion formation. While medial SMCs contribute to vascular lesions, the involvement of resident vascular stem cells (vSCs) remains unclear. We evaluated single cell photonics as a discriminator of cell phenotype in vitro before the presence of vSC within vascular lesions was assessed ex vivo using supervised machine learning and further validated using lineage tracing analysis. Using a novel lab-on-a-Disk(Load) platform, label-free single cell photonic emissions from normal and injured vessels ex vivo were interrogated and compared to freshly isolated aortic SMCs, cultured Movas SMCs, macrophages, B-cells, S100β+ mVSc, bone marrow derived mesenchymal stem cells (MSC) and their respective myogenic progeny across five broadband light wavelengths (λ465 - λ670 ± 20 nm). We found that profiles were of sufficient coverage, specificity, and quality to clearly distinguish medial SMCs from different vascular beds (carotid vs aorta), discriminate normal carotid medial SMCs from lesional SMC-like cells ex vivo following flow restriction, and identify SMC differentiation of a series of multipotent stem cells following treatment with transforming growth factor beta 1 (TGF- β1), the Notch ligand Jagged1, and Sonic Hedgehog using multivariate analysis, in part, due to photonic emissions from enhanced collagen III and elastin expression. Supervised machine learning supported genetic lineage tracing analysis of S100β+ vSCs and identified the presence of S100β+vSC-derived myogenic progeny within vascular lesions. We conclude disease-relevant photonic signatures may have predictive value for vascular disease.Graphical abstract

High-throughput label-free cell detection and counting from diffraction patterns with deep fully convolutional neural networks.

.Significance: Digital holographic microscopy (DHM) is a promising technique for the study of semitransparent biological specimen such as red blood cells (RBCs). It is important and meaningful to detect and count biological cells at the single cell level in biomedical images for biomarker discovery and disease diagnostics. However, the biological cell analysis based on phase information of images is inefficient due to the complexity of numerical phase reconstruction algorithm applied to raw hologram images. New cell study methods based on diffraction pattern directly are desirable.Aim: Deep fully convolutional networks (FCNs) were developed on raw hologram images directly for high-throughput label-free cell detection and counting to assist the biological cell analysis in the future.Approach: The raw diffraction patterns of RBCs were recorded by use of DHM. Ground-truth mask images were labeled based on phase images reconstructed from RBC holograms using numerical reconstruction algorithm. A deep FCN, which is UNet, was trained on the diffraction pattern images to achieve the label-free cell detection and counting.Results: The implemented deep FCNs provide a promising way to high-throughput and label-free counting of RBCs with a counting accuracy of 99% at a throughput rate of greater than 288 cells per second and field of view at the single cell level. Compared to convolutional neural networks, the FCNs can get much better results in terms of accuracy and throughput rate.Conclusions: High-throughput label-free cell detection and counting were successfully achieved from diffraction patterns with deep FCNs. It is a promising approach for biological specimen analysis based on raw hologram directly.

Single-Cell Stretching in Viscoelastic Fluids with Electronically Triggered Imaging for Cellular Mechanical Phenotyping

Cellular mechanical phenotypes in connection to physiological and pathological states of cells have become a promising intrinsic biomarker for label-free cell analysis in various biological research and medical diagnostics. In this work, we present a microfluidic system capable of high-throughput cellular mechanical phenotyping based on a rapid single-cell hydrodynamic stretching in a continuous viscoelastic fluid flow. Randomly introduced single cells are first aligned into a single streamline in viscoelastic fluids before being guided to a flow splitting junction for consistent hydrodynamic stretching. The arrival of individual cells prior to the flow splitting junction can be detected by an electrical sensing unit, which produces a triggering signal to activate a high-speed camera for on-demand imaging of the cell motion and deformation through the flow splitting junction. Cellular mechanical phenotypes, including cell size and cell deformability, are extracted from the analysis of these captured single-cell images. We have evaluated the sensitivity of the developed microfluidic mechanical phenotyping system by measuring the synthesized hydrogel microbeads with known Young's modulus. With this microfluidic cellular mechanical phenotyping system, we have revealed the statistical difference in the deformability of microfilament disrupted, normal, and fixed NIH 3T3 fibroblast cells. Furthermore, with the implementation of a machine-learning-based classification of MCF-10A and MDA-MB-231 mixtures, our label-free cellular phenotyping system has achieved a comparable cell analysis accuracy (0.9:1, 5.03:1) with respect to the fluorescence-based flow cytometry results (0.97:1, 5.33:1). The presented microfluidic mechanical phenotyping technique will open new avenues for high-throughput and label-free single-cell analysis in diverse biomedical applications.

Adhesion of Flk1-expressing cells under shear flow in phospholipid polymer-coated immunoaffinity channels

Label-free cell separation is a promising method in the field of stem-cell research to obtain desired cell populations. Here, we report on phospholipid polymer-coated microfluidic channels with immobilized antibodies as devices for the capture of cells expressing target antigens in a label-free manner. We fabricated a microfluidic channel containing immobilized antibodies against vascular endothelial growth factor receptor 2 (Flk1), a potential marker for cardiac, angiogenic, and hematopoietic cell regeneration. A series of investigations was carried out to elucidate the effect of the immobilized antibodies on the adhesion behavior of the Flk1-expressing cell subpopulation derived from induced pluripotent stem cells. Increasing the immobilized antibody density (0.18–5.0 × 109 ligands mm−2) led to an increased number of cells adhering to the channel. The antibody-immobilized polymer-coated surface suppressed nonspecific cell adhesion, which was swept away by a weak shear flow, and captured Flk1-expressing cells under a wall shear stress of 1.7 Pa. Flk1 expression was 2.8-fold higher in the cells that adhered than in those that did not adhere. Therefore, an optimal antibody density and sweeping flow are required for effective label-free separation of Flk1-positive cells.

Label-free cell phenotypic study of opioid receptors and discovery of novel mu opioid ligands from natural products

Ethnopharmacological relevanceMu opioid receptor (MOR) is mainly a drug target for analgesia. Opioid-like agonists such as morphine have been clinically used for analgesia but have potential adverse effects. MOR antagonists have been demonstrated to alleviate these side effects. Plants (Carthamus tinctorius L, Cynanchum otophyllum C. K. Schneid., Coffea arabica L., Prinsepia utilis Royle and Lepidium meyenii Walp.) and Ganoderma fungi (Ganoderma hainanense J. D. Zhao, Ganoderma capense (Lloyd) Teng, Ganoderma cochlear (Blume et Nees) Bres., Ganoderma resinaceum Boud and Ganoderma applanatum (Pers.) Pat.) are traditional medicines with beneficial effects on immunoregulation, analgesia and the nervous system, but whether MORs are engaged in their effects remains unknown. Aim of the studyThis work aimed to identify MOR ligands among compounds isolated from the above-mentioned 10 species, and to investigate selectivity against four opioid receptor subtypes. By analyzing the structure-activity relationship and off-target effects, we could provide a new direction for the future development of MOR drugs. Materials and methodsFour opioid receptor subtype models, including MOR, delta (DOR), kappa (KOR) and nop (NOR), were established with a label-free phenotypic dynamic mass redistribution assay to systematically profile the pharmacological properties of known ligands. Then, 82 natural compounds derived from the 10 species were screened against MOR to identify new ligands. The selectivity of the new ligands was characterized against the four subtypes, and off-target effects were also investigated on eight G protein-coupled receptors (GPCRs). ResultsThe pharmacological properties of known ligands on transfected HEK293T-MOR, HEK293-DOR, HEK293-KOR and HEK293-NOR cell lines were characterized. Seven compounds purified from Ganoderma cochlear (Blume et Nees) Bres. and Carthamus tinctorius L were MOR antagonists with micromolar potency. Among them, compound 35 showed the strongest antagonistic activity on MOR with an IC50 value of 10.0 ± 3.0 μM. To a certain extent, these seven new antagonists, exhibited antagonistic activity on the other opioid receptor subtypes, and they had almost no effect on other GPCRs, including CB1, CB2, M2 and beta2AR. Additionally, a compound from Lepidium meyenii Walp. displayed MOR agonistic activity. ConclusionsThe established screening models opened new avenues for the discovery and evaluation of opioid receptor ligand selectivity. Together, the novel MOR antagonists and agonists will enrich the inventory of MOR ligands and benefit related therapies.

Impedimetric Microfluidic Sensor-in-a-Tube for Label-Free Immune Cell Analysis.

Analytical platforms based on impedance spectroscopy are promising for non-invasive and label-free analysis of single cells as well as of their extracellular matrix, being essential to understand cell function in the presence of certain diseases. Here, an innovative rolled-up impedimetric microfulidic sensor, called sensor-in-a-tube, is introduced for the simultaneous analysis of single human monocytes CD14+ and their extracellular medium upon liposaccharides (LPS)-mediated activation. In particular, rolled-up platinum microelectrodes are integrated within for the static and dynamic (in-flow) detection of cells and their surrounding medium (containing expressed cytokines) over an excitation frequency range from 102 to 5 × 106 Hz. The correspondence between cell activation stages and the electrical properties of the cell surrounding medium have been detected by electrical impedance spectroscopy in dynamic mode without employing electrode surface functionalization or labeling. The designed sensor-in-a-tube platform is shown as a sensitive and reliable tool for precise single cell analysis toward immune-deficient diseases diagnosis.

Holotomography: Refractive Index as an Intrinsic Imaging Contrast for 3-D Label-Free Live Cell Imaging.

Live cell imaging provides essential information in the investigation of cell biology and related pathophysiology. Refractive index (RI) can serve as intrinsic optical imaging contrast for 3-D label-free and quantitative live cell imaging, and provide invaluable information to understand various dynamics of cells and tissues for the study of numerous fields. Recently significant advances have been made in imaging methods and analysis approaches utilizing RI, which are now being transferred to biological and medical research fields, providing novel approaches to investigate the pathophysiology of cells. To provide insight into how RI can be used as an imaging contrast for imaging of biological specimens, here we provide the basic principle of RI-based imaging techniques and summarize recent progress on applications, ranging from microbiology, hematology, infectious diseases, hematology, and histopathology.

Multi-frequency single cell electrical impedance measurement for label-free cell viability analysis.

Cell viability is a physiological status connected to cell membrane integrity and cytoplasmic topography, which is profoundly important for fundamental biological research and practical biomedical applications. A conventional method for assessing cell viability is through cell staining analysis. However, cell staining involves laborious and complicated processing procedures and is normally cytotoxic. Intrinsic cellular phenotypes thus provide new avenues for measuring cell viability in a stain-free and non-toxic manner. In this work, we present a label-free non-destructive impedance-based approach for cell viability assessment by simultaneously characterizing multiple electrical cellular phenotypes in a high-throughput manner (>1000 cells per min). A novel concept called the complex opacity spectrum is introduced for improving the discrimination of live and dead cells. The analysis of the complex opacity spectrum leads to the discovery of two frequency ranges that are optimized for characterizing membranous and cytoplasmic electrical phenotypes. The present impedance-based approach has successfully discriminated between living and dead cells in two different experimental scenarios, including mixed living and dead cells in both homogenous and heterogeneous cell samples. This impedance-based single cell phenotyping technique provides highly accurate and consistent cell viability analysis, which has been validated by commercial fluorescence-based flow cytometry (∼1% difference) using heterogeneous cell samples. This label-free high-throughput cell viability analysis strategy will have broad applications in the field of biology and medicine.

Label-Free Separation of Living Bacteria and Eukaryotic Cells by a Novel Microfluidic Dielectrophoresis Integrated Chip System

Microfluidic dielectrophoresis (DEP) technology has been applied to many devices to perform label-free target cell separation. Cells separated by these devices are used in laboratories, mainly for medical research. The present study designed a microfluidic DEP device to fabricate a rapid and semiautomated cell separation system in conjunction with microscopy to enumerate the separated cells. With this device, we efficiently segregated bacterial cells from liquid products and enriched one cell type from two mixed eukaryotic cell types. The device eliminated sample pretreatment andestablished cell separation by all-in-one operation in a lab-on-chip, requiring only a small sample volume (0.5–1 mL) to enumerate the target cells and completing the entire separation process within thirty minutes. Such a rapid cell separation technique is in high demand by many researchers to promptly characterize the target cells.