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Significance We have developed a unique approach for the separation of particles and biological cells through standing surface acoustic waves oriented at an optimum angle to the fluid flow direction in a microfluidic device. This experimental setup, optimized by systematic analyses, has been used to demonstrate effective separation based on size, compressibility, and mechanical properties of particles and cells. The potential of this method for biological–biomedical applications was demonstrated through the example of isolating MCF-7 breast cancer cells from white blood cells. The method offers a possible route for label-free particle or cell separation for many applications in research, disease diagnosis, and drug-efficacy assessment.

Cell separation and sorting are essential steps in cell biology research and in many diagnostic and therapeutic methods. Recently, there has been interest in methods which avoid the use of biochemical labels; numerous intrinsic biomarkers have been explored to identify cells including size, electrical polarizability, and hydrodynamic properties. This review highlights microfluidic techniques used for label-free discrimination and fractionation of cell populations. Microfluidic systems have been adopted to precisely handle single cells and interface with other tools for biochemical analysis. We analyzed many of these techniques, detailing their mode of separation, while concentrating on recent developments and evaluating their prospects for application. Furthermore, this was done from a perspective where inertial effects are considered important and general performance metrics were proposed which would ease comparison of reported technologies. Lastly, we assess the current state of these technologies and suggest directions which may make them more accessible. [graphic removed]

Circulating tumor cells (CTCs) are important targets for cancer biology studies. To further elucidate the role of CTCs in cancer metastasis and prognosis, effective methods for isolating extremely rare tumor cells from peripheral blood must be developed. Acoustic-based methods, which are known to preserve the integrity, functionality, and viability of biological cells using label-free and contact-free sorting, have thus far not been successfully developed to isolate rare CTCs using clinical samples from cancer patients owing to technical constraints, insufficient throughput, and lack of long-term device stability. In this work, we demonstrate the development of an acoustic-based microfluidic device that is capable of high-throughput separation of CTCs from peripheral blood samples obtained from cancer patients. Our method uses tilted-angle standing surface acoustic waves. Parametric numerical simulations were performed to design optimum device geometry, tilt angle, and cell throughput that is more than 20 times higher than previously possible for such devices. We first validated the capability of this device by successfully separating low concentrations (∼100 cells/mL) of a variety of cancer cells from cell culture lines from WBCs with a recovery rate better than 83%. We then demonstrated the isolation of CTCs in blood samples obtained from patients with breast cancer. Our acoustic-based separation method thus offers the potential to serve as an invaluable supplemental tool in cancer research, diagnostics, drug efficacy assessment, and therapeutics owing to its excellent biocompatibility, simple design, and label-free automated operation while offering the capability to isolate rare CTCs in a viable state. Significance The separation and analysis of circulating tumor cells (CTCs) provides physicians a minimally invasive way to monitor the response of cancer patients to various treatments. Among the existing cell-separation methods, acoustic-based approaches provide significant potential to preserve the phenotypic and genotypic characteristics of sorted cells, owing to their safe, label-free, and contactless nature. In this work, we report the development of an acoustic-based device that successfully demonstrates the isolation of rare CTCs from the clinical blood samples of cancer patients. Our work thus provides a unique means to obtain viable and undamaged CTCs, which can subsequently be cultured. The results presented here offer unique pathways for better cancer diagnosis, prognosis, therapy monitoring, and metastasis research.

In fluorescence-activated cell sorting, characteristic target features are labeled with a specific fluorophore, and cells displaying different fluorophores are sorted. Ota et al. describe a technique called ghost cytometry that allows cell sorting based on the morphology of the cytoplasm, labeled with a single-color fluorophore. The motion of cells relative to a patterned optical structure provides spatial information that is compressed into temporal signals, which are sequentially measured by a single-pixel detector. Images can be reconstructed from this spatial and temporal information, but this is computationally costly. Instead, using machine learning, cells are classified directly from the compressed signals, without reconstructing an image. The method was able to separate morphologically similar cell types in an ultrahigh-speed fluorescence imaging–activated cell sorter. Science , this issue p. 1246 Morphology-based cell classification and sorting is achieved at high accuracy and throughput without obtaining images. Ghost imaging is a technique used to produce an object’s image without using a spatially resolving detector. Here we develop a technique we term “ghost cytometry,” an image-free ultrafast fluorescence “imaging” cytometry based on a single-pixel detector. Spatial information obtained from the motion of cells relative to a static randomly patterned optical structure is compressively converted into signals that arrive sequentially at a single-pixel detector. Combinatorial use of the temporal waveform with the intensity distribution of the random pattern allows us to computationally reconstruct cell morphology. More importantly, we show that applying machine-learning methods directly on the compressed waveforms without image reconstruction enables efficient image-free morphology-based cytometry. Despite a compact and inexpensive instrumentation, image-free ghost cytometry achieves accurate and high-throughput cell classification and selective sorting on the basis of cell morphology without a specific biomarker, both of which have been challenging to accomplish using conventional flow cytometers.

The importance of early cancer diagnosis and improved cancer therapy has been clear for years and has initiated worldwide research towards new possibilities in the care strategy of patients with cancer using technological innovations. One of the key research fields involves the separation and detection of circulating tumor cells (CTC) because of their suggested important role in early cancer diagnosis and prognosis, namely, providing easy access by a liquid biopsy from blood to identify metastatic cells before clinically detectable metastasis occurs and to study the molecular and genetic profile of these metastatic cells. Provided the opportunity to further progress the development of technology for treating cancer, several CTC technologies have been proposed in recent years by various research groups and companies. Despite their potential role in cancer healthcare, CTC methods are currently mainly used for research purposes, and only a few methods have been accepted for clinical application because of the difficulties caused by CTC heterogeneity, CTC separation from the blood, and a lack of thorough clinical validation. Therefore, the standardization and clinical application of various developed CTC technologies remain important subsequent necessary steps. Because of their suggested future clinical benefits, we focus on describing technologies using whole blood samples without any pretreatment and discuss their advantages, use, and significance. Technologies using whole blood samples utilize size-based, immunoaffinity-based, and density-based methods or combinations of these methods as well as positive and negative enrichment during separation. Although current CTC technologies have not been truly implemented yet, they possess high potential as future clinical diagnostic techniques for the individualized therapy of patients with cancer. Thus, a detailed discussion of the clinical suitability of these new advanced technologies could help prepare clinicians for the future and can be a foundation for technologies that would be used to eliminate CTCs in vivo.

The handling of single cells is of great importance in applications such as cell line development or single-cell analysis, e.g., for cancer research or for emerging diagnostic methods. This review provides an overview of technologies that are currently used or in development to isolate single cells for subsequent single-cell analysis. Data from a dedicated online market survey conducted to identify the most relevant technologies, presented here for the first time, shows that FACS (fluorescence activated cell sorting) respectively Flow cytometry (33% usage), laser microdissection (17%), manual cell picking (17%), random seeding/dilution (15%), and microfluidics/lab-on-a-chip devices (12%) are currently the most frequently used technologies. These most prominent technologies are described in detail and key performance factors are discussed. The survey data indicates a further increasing interest in single-cell isolation tools for the coming years. Additionally, a worldwide patent search was performed to screen for emerging technologies that might become relevant in the future. In total 179 patents were found, out of which 25 were evaluated by screening the title and abstract to be relevant to the field.

Imaging flow cytometry allows image-activated cell sorting (IACS) with enhanced feature dimensions in cellular morphology, structure, and composition. However, existing IACS frameworks suffer from the challenges of 3D information loss and processing latency dilemma in real-time sorting operation. Herein, we establish a neuromorphic-enabled video-activated cell sorter (NEVACS) framework, designed to achieve high-dimensional spatiotemporal characterization content alongside high-throughput sorting of particles in wide field of view. NEVACS adopts event camera, CPU, spiking neural networks deployed on a neuromorphic chip, and achieves sorting throughput of 1000 cells/s with relatively economic hybrid hardware solution (~$10 K for control) and simple-to-make-and-use microfluidic infrastructures. Particularly, the application of NEVACS in classifying regular red blood cells and blood-disease-relevant spherocytes highlights the accuracy of using video over a single frame (i.e., average error of 0.99% vs 19.93%), indicating NEVACS’ potential in cell morphology screening and disease diagnosis. Existing image-activated cell sorting tools suffer from the challenges of 3D information loss and processing latency in real-time sorting operations. Here, the authors propose a neuromorphic-enabled video-activated cell sorter (NEVACS) framework, which achieves high-dimensional spatiotemporal characterization content and high-throughput sorting of particles.

The advent of image-activated cell sorting and imaging-based cell picking has advanced our knowledge and exploitation of biological systems in the last decade. Unfortunately, they generally rely on fluorescent labeling for cellular phenotyping, an indirect measure of the molecular landscape in the cell, which has critical limitations. Here we demonstrate Raman image-activated cell sorting by directly probing chemically specific intracellular molecular vibrations via ultrafast multicolor stimulated Raman scattering (SRS) microscopy for cellular phenotyping. Specifically, the technology enables real-time SRS-image-based sorting of single live cells with a throughput of up to ~100 events per second without the need for fluorescent labeling. To show the broad utility of the technology, we show its applicability to diverse cell types and sizes. The technology is highly versatile and holds promise for numerous applications that are previously difficult or undesirable with fluorescence-based technologies. Most current cell sorting methods are based on fluorescence detection with no imaging capability. Here the authors generate and use Raman image-activated cell sorting with a throughput of around 100 events per second, providing molecular images with no need for labeling.

We introduce Digital microfluidic Isolation of Single Cells for -Omics (DISCO), a platform that allows users to select particular cells of interest from a limited initial sample size and connects single-cell sequencing data to their immunofluorescence-based phenotypes. Specifically, DISCO combines digital microfluidics, laser cell lysis, and artificial intelligence-driven image processing to collect the contents of single cells from heterogeneous populations, followed by analysis of single-cell genomes and transcriptomes by next-generation sequencing, and proteomes by nanoflow liquid chromatography and tandem mass spectrometry. The results described herein confirm the utility of DISCO for sequencing at levels that are equivalent to or enhanced relative to the state of the art, capable of identifying features at the level of single nucleotide variations. The unique levels of selectivity, context, and accountability of DISCO suggest potential utility for deep analysis of any rare cell population with contextual dependencies. Multi-Omic approaches are a powerful way for obtaining in-depth understanding of a cell’s state. Here the authors present DISCO, combining digital microfluidics, laser cell lysis, and artificial intelligence-driven image processing to analyze single-cell genomes, transcriptomes and proteomes in a mixed population.

Autologous therapies using adipose-derived stromal vascular fraction (AD-SVFs) and adult adipose-derived mesenchymal stem cells (AD-MSCs) warrant careful preparation of the harvested adipose tissue. Currently, no standardized technique for this preparation exists. Processing quantitative standards (PQSs) define manufacturing quantitative variables (such as time, volume, and pressure). Processing qualitative standards (PQLSs) define the quality of the materials and methods in manufacturing. The purpose of the review was to use PQSs and PQLSs to report the in vivo and in vitro results obtained by different processing kits that use different procedures (enzymatic vs. non-enzymatic) to isolate human AD-SVFs/AD-MSCs. PQSs included the volume of fat tissue harvested and reagents used, the time/gravity of centrifugation, and the time, temperature, and tilt level/speed of incubation and/or centrifugation. PQLSs included the use of a collagenase, a processing time of 30 min, kit weight, transparency of the kit components, the maintenance of a closed sterile processing environment, and the use of a small centrifuge and incubating rocker. Using a kit with the PQSs and PQLSs described in this study enables the isolation of AD-MSCs that meet the consensus quality criteria. As the discovery of new critical quality attributes (CQAs) of AD-MSCs evolve with respect to purity and potency, adjustments to these benchmark PQSs and PQLs will hopefully isolate AD-MSCs of various CQAs with greater reproducibility, quality, and safety. Confirmatory studies will no doubt need to be completed.