Automated single cell isolation from suspension with computer vision

Intelligent image-activated sorting of Chlamydomonas reinhardtii by mitochondrial localization

Organelle positioning in cells is associated with various metabolic functions and signaling in unicellular organisms. Specifically, the microalga Chlamydomonas reinhardtii repositions its mitochondria, depending on the levels of inorganic carbon. Mitochondria are typically randomly distributed in the Chlamydomonas cytoplasm, but relocate toward the cell periphery at low inorganic carbon levels. This mitochondrial relocation is linked with the carbon-concentrating mechanism, but its significance is not yet thoroughly understood. A genotypic understanding of this relocation would require a high-throughput method to isolate rare mutant cells not exhibiting this relocation. However, this task is technically challenging due to the complex intracellular morphological difference between mutant and wild-type cells, rendering conventional non-image-based high-event-rate methods unsuitable. Here, we report our demonstration of intelligent image-activated cell sorting by mitochondrial localization. Specifically, we applied an intelligent image-activated cell sorting system to sort for C. reinhardtii cells displaying no mitochondrial relocation. We trained a convolutional neural network (CNN) to distinguish the cell types based on the complex morphology of their mitochondria. The CNN was employed to perform image-activated sorting for the mutant cell type at 180 events per second, which is 1-2 orders of magnitude faster than automated microscopy with robotic pipetting, resulting in an enhancement of the concentration from 5% to 56.5% corresponding to an enrichment factor of 11.3. These results show the potential of image-activated cell sorting for connecting genotype-phenotype relations for rare-cell populations, which require a high throughput and could lead to a better understanding of metabolic functions in cells.

Plasmonic Biosensing for Label-Free Detection of Two Hallmarks of Cancer Cells: Cell-Matrix Interaction and Cell Division

Two key features of cancer cells are sustained proliferation and invasion, which is preceded by a modification of the adhesion properties to the extracellular matrix. Currently, fluorescence-based techniques are mainly used to detect these processes, including flow cytometry and fluorescence resonance energy transfer (FRET) microscopy. We have previously described a simple, fast and label-free method based on a gold nanohole array biosensor to detect the spectral response of single cells, which is highly dependent on the actin cortex. Here we used this biosensor to study two cellular processes where configuration of the actin cortex plays an essential role: cell cycle and cell–matrix adhesion. Colorectal cancer cells were maintained in culture under different conditions to obtain cells stopped either in G0/G1 (resting cells/cells at the initial steps of cell growth) or G2 (cells undergoing division) phases of the cell cycle. Data from the nanohole array biosensor showed an ability to discriminate between both cell populations. Additionally, cancer cells were monitored with the biosensor during the first 60 min after cells were deposited onto a biosensor coated with fibronectin, an extracellular matrix protein. Spectral changes were detected in the first 20 min and increased over time as the cell–biosensor contact surface increased. Our data show that the nanohole array biosensor provides a label-free and real-time procedure to detect cells undergoing division or changes in cell–matrix interaction in both clinical and research settings.

Label-free imaging flow cytometry for analysis and sorting of enzymatically dissociated tissues

Biomedical research relies on identification and isolation of specific cell types using molecular biomarkers and sorting methods such as fluorescence or magnetic activated cell sorting. Labelling processes potentially alter the cells’ properties and should be avoided, especially when purifying cells for clinical applications. A promising alternative is the label-free identification of cells based on physical properties. Sorting real-time deformability cytometry (soRT-DC) is a microfluidic technique for label-free analysis and sorting of single cells. In soRT-FDC, bright-field images of cells are analyzed by a deep neural net (DNN) to obtain a sorting decision, but sorting was so far only demonstrated for blood cells which show clear morphological differences and are naturally in suspension. Most cells, however, grow in tissues, requiring dissociation before cell sorting which is associated with challenges including changes in morphology, or presence of aggregates. Here, we introduce methods to improve robustness of analysis and sorting of single cells from nervous tissue and provide DNNs which can distinguish visually similar cells. We employ the DNN for image-based sorting to enrich photoreceptor cells from dissociated retina for transplantation into the mouse eye.

Isolating and culturing of single microbial cells by laser ejection sorting technology

Single cell isolation and cultivation play an important role in studying physiology, gene expression and functions of microorganisms. A series of single-cell isolation technologies have been developed, among which single-cell ejection technology is one of the most promising. Single cell ejection technology has applied Laser Induced Forward Transfer Technique (LIFT) to isolate bacteria but the viability (or recovery rate) of cells after sorting has not been clarified in the current research progress. In this work, to keep the cells alive as much as possible, we propose a three-layer LIFT system (top layer: 25-nm aluminum film; second layer: 3 μm agar media; third layer: liquid containing bacterial) for the isolation and cultivation of single Gram-negative (E. coli), Gram-positive (Lactobacillus rhamnosus GG, LGG), and eukaryotic microorganisms (Saccharomyces cerevisiae). The experiment results showed that the average survival rates for ejected pure single cells were 63% for Saccharomyces cerevisiae, 22% for E. coli DH5α, and 74% for LGG. In addition, we successfully isolated and cultured the GFP expressing E. coli JM109 from the mixture containing complex communities of soil bacteria by fluorescence signal. The average survival rate of E. coli JM109 was demonstrated to be 25.3%. In this study, the isolated and cultured single colonies were further confirmed by colony PCR and sequencing. Such precise sorting and cultivation technique of live single microbial cells could be coupled with other microscopic approaches to isolate single microorganisms with specific functions, revealing their roles in the natural community. Importance We developed a laser induced forward transfer (LIFT) technology to accurately isolate single live microbial cells. The cultivation recovery rates of the ejected single cells were 63% for Saccharomyces cerevisiae, 22% for E. coli DH5α, and 74% for Lactobacillus rhamnosus GG (LGG). Coupled LIFT with fluorescent microscope, we demonstrated that single cells of GFP expressing E. coli JM109 were sorted according to fluorescence signal from a complex community of soil bacteria, and subsequently cultured with 25% cultivation recovery rate. This single cell live sorting technology could isolate single microbes with specific functions, revealing their roles in the natural community.

Nanonewton scale adhesion force measurements on biotinylated microbeads with a robotic micropipette

Binding force between biomolecules has a crucial role in most biological processes. Receptor-ligand interactions transmit physical forces and signals simultaneously. Previously, we employed a robotic micropipette both in live cell and microbead adhesion studies to explore the adhesion force of biomolecules such as cell surface receptors including specific integrins on immune cells. Here we apply standard computational fluid dynamics simulations to reveal the detailed physical background of the flow generated by the micropipette when probing microbead adhesion on functionalized surfaces. Measuring the aspiration pressure needed to pick up the biotinylated 10 μm beads on avidin coated surfaces and converting it to a hydrodynamic lifting force on the basis of simulations, we found an unbinding force of 12 ± 2 nN, when targeting the beads manually; robotic targeting resulted in 9 ± 4 nN (mean ± SD). We measured and simulated the effect of the targeting offset, when the microbead was out of the axis (off-axis)of the micropipette. According to the simulations, the higher offset resulted in a higher lifting force acting on the bead. Considering this effect, we could readily correct the impact of the targeting offset to renormalize the experimental data. Horizontal force and torque also appeared in simulations in case of a targeting offset. Surprisingly, simulations show that the lifting force acting on the bead reaches a maximum at a flow rate of ~ 5 μl/s if the targeting offset is not very high (<5 μm). Further increasing the flow rate decreases the lifting force. We attribute this effect to the spherical geometry of the bead. We predict that higher flow rates cannot increase the hydrodynamic lifting force acting on the precisely targeted microbead, setting a fundamental force limit (16 nN in our setup) for manipulating microbeads with a micropipette perpendicular to the supporting surface. In order to extend the force range, we propose the offset targeting of microbeads.

Simple-to-Operate Approach for Single Cell Analysis Using a Hydrophobic Surface and Nanosized Droplets

Microfluidics-based technologies for single-cell analysis are becoming increasingly important tools in biological studies. With the increasing sophistication of microfluidics, cellular barcoding techniques, and next-generation sequencing, a more detailed picture of cellular subtype is emerging. Unfortunately, the majority of the methods developed for single-cell analysis are high-throughput and not suitable for rare cell analysis as they require a high input cell number. Here, we report a low-cost and reproducible method for rare single-cell analysis using a highly hydrophobic surface and nanosized static droplets. Our method allows rapid and efficient on-chip single-cell lysis and subsequent collection of genetic materials in nanoliter droplets using a micromanipulator or a laboratory pipette before subsequent genetic analysis. We show precise isolation of single cancer cells with high purity using two different strategies (i- cytospin and ii- static droplet array) for subsequent RNA analysis using droplet digital polymerase chain reaction (PCR) and real-time PCR. Our highly controlled isolation method opens a new avenue for the study of subcellular functional mechanisms, enabling the identification of rare cells of potential functional or pathogenic consequence.

Circulating Metabolites as Potential Biomarkers for Neurological Disorders-Metabolites in Neurological Disorders

There are, still, limitations to predicting the occurrence and prognosis of neurological disorders. Biomarkers are molecules that can change in different conditions, a feature that makes them potential tools to improve the diagnosis of disease, establish a prognosis, and monitor treatments. Metabolites can be used as biomarkers, and are small molecules derived from the metabolic process found in different biological media, such as tissue samples, cells, or biofluids. They can be identified using various strategies, targeted or untargeted experiments, and by different techniques, such as high-performance liquid chromatography, mass spectrometry, or nuclear magnetic resonance. In this review, we aim to discuss the current knowledge about metabolites as biomarkers for neurological disorders. We will present recent developments that show the need and the feasibility of identifying such biomarkers in different neurological disorders, as well as discuss relevant research findings in the field of metabolomics that are helping to unravel the mechanisms underlying neurological disorders. Although several relevant results have been reported in metabolomic studies in patients with neurological diseases, there is still a long way to go for the clinical use of metabolites as potential biomarkers in these disorders, and more research in the field is needed.

The differential role of CR3 (CD11b/CD18) and CR4 (CD11c/CD18) in the adherence, migration and podosome formation of human macrophages and dendritic cells under inflammatory conditions

CR3 and CR4, the leukocyte specific β2-integrins, involved in cellular adherence, migration and phagocytosis, are often assumed to have similar functions. Previously however, we proved that under physiological conditions CR4 is dominant in the adhesion to fibrinogen of human monocyte-derived macrophages (MDMs) and dendritic cells (MDDCs). Here, using inflammatory conditions, we provide further evidence that the expression and function of CR3 and CR4 are not identical in these cell types. We found that LPS treatment changes their expression differently on MDMs and MDDCs, suggesting a cell type specific regulation. Using mAb24, specific for the high affinity conformation of CD18, we proved that the activation and recycling of β2-integrins is significantly enhanced upon LPS treatment. Adherence to fibrinogen was assessed by two fundamentally different approaches: a classical adhesion assay and a computer-controlled micropipette, capable of measuring adhesion strength. While both receptors participated in adhesion, we demonstrated that CR4 exerts a dominant role in the strong attachment of MDDCs. Studying the formation of podosomes we found that MDMs retain podosome formation after LPS activation, whereas MDDCs lose this ability, resulting in a significantly reduced adhesion force and an altered cellular distribution of CR3 and CR4. Our results suggest that inflammatory conditions reshape differentially the expression and role of CR3 and CR4 in macrophages and dendritic cells.

Imaging-assisted hydrogel formation for single cell isolation

We report a flexible single-cell isolation method by imaging-assisted hydrogel formation. Our approach consists of imaging-aided selective capture of cells of interest by encasing them into a polymeric hydrogel, followed by removal of unwanted cells and subsequent release of isolated cells by enzymatic hydrogel degradation, thus offering an opportunity for further analysis or cultivation of selected cells. We achieved high sorting efficiency and observed excellent viability rates (>98%) for NIH/3T3 fibroblasts and A549 carcinoma cells isolated using this procedure. The method presented here offers a mask-free, cost-efficient and easy-to-use alternative to many currently existing surface-based cell-sorting techniques, and has the potential to impact the field of cell culturing and isolation, e.g. single cell genomics and proteomics, investigation of cellular heterogeneity and isolation of best performing mutants for developing new cell lines.

Existing and Emerging Metabolomic Tools for ALS Research

Growing evidence suggests that aberrant energy metabolism could play an important role in the pathogenesis of amyotrophic lateral sclerosis (ALS). Despite this, studies applying advanced technologies to investigate energy metabolism in ALS remain scarce. The rapidly growing field of metabolomics offers exciting new possibilities for ALS research. Here, we review existing and emerging metabolomic tools that could be used to further investigate the role of metabolism in ALS. A better understanding of the metabolic state of motor neurons and their surrounding cells could hopefully result in novel therapeutic strategies.

Three-Dimensional Autofocusing Visual Feedback for Automated Rare Cells Sorting in Fluorescence Microscopy

Sorting rare cells from heterogeneous mixtures makes a significant contribution to biological research and medical treatment. However, the performances of traditional methods are limited due to the time-consuming preparation, poor purity, and recovery rate. In this paper, we proposed a cell screening method based on the automated microrobotic aspirate-and-place strategy under fluorescence microscopy. A fast autofocusing visual feedback (FAVF) method is introduced for precise and real-time three-dimensional (3D) location. In the context of this method, the scalable correlation coefficient (SCC) matching is presented for planar locating cells with regions of interest (ROI) created for autofocusing. When the overlap occurs, target cells are separated by a segmentation algorithm. To meet the shallow depth of field (DOF) limitation of the microscope, the improved multiple depth from defocus (MDFD) algorithm is used for depth detection, taking 850 ms a time with an accuracy rate of 96.79%. The neighborhood search based algorithm is applied for the tracking of the micropipette. Finally, experiments of screening NIH/3T3 (mouse embryonic fibroblast) cells verifies the feasibility and validity of this method with an average speed of 5 cells/min, 95% purity, and 80% recovery rate. Moreover, such versatile functions as cell counting and injection, for example, could be achieved by this expandable system.

A practical review on the measurement tools for cellular adhesion force

Cell-cell and cell-matrix adhesions are fundamental in all multicellular organisms. They play a key role in cellular growth, differentiation, pattern formation and migration. Cell-cell adhesion is substantial in the immune response, pathogen-host interactions, and tumor development. The success of tissue engineering and stem cell implantations strongly depends on the fine control of live cell adhesion on the surface of natural or biomimetic scaffolds. Therefore, the quantitative and precise measurement of the adhesion strength of living cells is critical, not only in basic research but in modern technologies, too. Several techniques have been developed or are under development to quantify cell adhesion. All of them have their pros and cons, which has to be carefully considered before the experiments and interpretation of the recorded data. Current review provides a guide to choose the appropriate technique to answer a specific biological question or to complete a biomedical test by measuring cell adhesion.

Current Trends of Microfluidic Single-Cell Technologies

The investigation of human disease mechanisms is difficult due to the heterogeneity in gene expression and the physiological state of cells in a given population. In comparison to bulk cell measurements, single-cell measurement technologies can provide a better understanding of the interactions among molecules, organelles, cells, and the microenvironment, which can aid in the development of therapeutics and diagnostic tools. In recent years, single-cell technologies have become increasingly robust and accessible, although limitations exist. In this review, we describe the recent advances in single-cell technologies and their applications in single-cell manipulation, diagnosis, and therapeutics development.

A single-cell translocation and secretion assay (TransSeA)

Understanding biological heterogeneity at the single cell level is required for advancing insights into the complexity of human physiology and diseases. While advances in technological and analytical methods have afforded unprecedented glimpses of this heterogeneity, the information captured to date largely represents one-time "snap" shots of single cell physiology. To address the limits of existing methods and to accelerate discoveries from single cell studies, we developed a single-cell translocation and secretion assay (TransSeA) that supports time lapse analysis, enables molecular cargo analysis of secretions such as extracellular vesicles (EVs) from single cells, allows massively parallel single cell transfer according to user-defined cell selection criteria, and supports tracking of phenotypes between parental and progeny cells derived from single cells. To demonstrate the unique capabilities and efficiencies of the assay, we present unprecedented single cell studies related to cell secretions, EV cargos and cell intrinsic properties. Although used as examples to demonstrate the feasibility and versatility of the technology, the studies already provided insights into key unanswered questions such as the microRNAs carried by EVs, the relationships between EV secretion rates and gene expressions, and the spontaneous, trans-generational phenotypic changes in EV secretion between parental and progeny cells.

Software tools for cell culture-related 3D printed structures

Three-dimensional (3D) printing technology allowed fast and cheap prototype fabrication in numerous segments of industry and it also became an increasingly versatile experimental platform in life sciences. Yet, general purpose software tools to control printer hardware are often suboptimal for bioprinting applications. Here we report a package of open source software tools that we developed specifically to meet bioprinting requirements: Machine movements can be (i) precisely specified using high level programming languages, and (ii) easily distributed across a batch of tissue culture dishes. To demonstrate the utility of the reported technique, we present custom fabricated, biocompatible 3D-printed plastic structures that can control cell spreading area or medium volume, and exhibit excellent optical properties even at 50 ul sample volumes. We expect our software tools to be helpful not only to manufacture customized in vitro experimental chambers, but for applications involving printing cells and extracellular matrices as well.

CD11c/CD18 Dominates Adhesion of Human Monocytes, Macrophages and Dendritic Cells over CD11b/CD18

Complement receptors CR3 (CD11b/CD18) and CR4 (CD11c/CD18) belong to the family of beta2 integrins and are expressed mainly by myeloid cell types in humans. Previously, we proved that CR3 rather than CR4 plays a key role in phagocytosis. Here we analysed how CD11b and CD11c participate in cell adhesion to fibrinogen, a common ligand of CR3 and CR4, employing human monocytes, monocyte-derived macrophages (MDMs) and monocyte-derived dendritic cells (MDDCs) highly expressing CD11b as well as CD11c. We determined the exact numbers of CD11b and CD11c on these cell types by a bead-based technique, and found that the ratio of CD11b/CD11c is 1.2 for MDDCs, 1.7 for MDMs and 7.1 for monocytes, suggesting that the function of CD11c is preponderant in MDDCs and less pronounced in monocytes. Applying state-of-the-art biophysical techniques, we proved that cellular adherence to fibrinogen is dominated by CD11c. Furthermore, we found that blocking CD11b significantly enhances the attachment of MDDCs and MDMs to fibrinogen, demonstrating a competition between CD11b and CD11c for this ligand. On the basis of the cell surface receptor numbers and the measured adhesion strength we set up a model, which explains the different behavior of the three cell types.

Trapping and isolation of single prokaryotic cells in a micro-chamber array using dielectrophoresis

The vast majority of prokaryotic species are difficult or impossible to culture in laboratories, which makes it difficult to study these organisms using conventional biochemical techniques.