Dye free automated cell counting and analysis

Label-free conduction velocity mapping and gap junction assessment of functional iPSC-Cardiomyocyte monolayers

Cardiac conduction is an important function of the heart. To date, accurate measurement of conduction velocity (CV) in vitro is hindered by the low spatial resolution and poor signal-to-noise ratio of microelectrode arrays (MEAs), or the cytotoxicity and end-point analysis of fluorescence optical imaging. Here, we have developed a new label-free method based on defocused brightfield imaging to quantify CV by analyzing centroid displacements and contraction trajectories of each cardiomyocyte in a monolayer of human stem cell-derived cardiomyocytes (iPSC-CMs). Our data revealed that the time delay between intracellular calcium release and the initiation of cell contraction is highly consistent across cardiomyocytes; however, the duration a cell takes to reach its maximum beating magnitude varies significantly, proving that the time delay in excitation-contraction coupling is largely constant in iPSC-CMs. Standard calcium imaging of the same iPSC-CM populations (~10⁶ cells) was conducted for comparison with our label-free method. The results confirmed that our label-free method was capable of achieving highly accurate CV mapping (17.64 ± 0.89 cm/s vs. 17.95 ± 2.29 cm/s, p-value>0.1). Additionally, our method effectively revealed various shapes in cell beating pattern. We also performed label-free CV mapping on disease-specific iPSC-CM monolayers with plakophilin-2 (PKP2) knockdown, which effectively quantified their low CV values and further validated the arrhythmogenic role of PKP2 mutation in arrhythmogenic right ventricular cardiomyopathy (ARVC) through the disruption of cardiac conduction. The label-free method offers a cytotoxic-free technique for long-term measurement of dynamic beating trajectories, beating propagation and conduction velocities of cardiomyocyte monolayers.

Iterative unsupervised domain adaptation for generalized cell detection from brightfield z-stacks

BACKGROUND

Cell counting from cell cultures is required in multiple biological and biomedical research applications. Especially, accurate brightfield-based cell counting methods are needed for cell growth analysis. With deep learning, cells can be detected with high accuracy, but manually annotated training data is required. We propose a method for cell detection that requires annotated training data for one cell line only, and generalizes to other, unseen cell lines.

RESULTS

Training a deep learning model with one cell line only can provide accurate detections for similar unseen cell lines (domains). However, if the new domain is very dissimilar from training domain, high precision but lower recall is achieved. Generalization capabilities of the model can be improved with training data transformations, but only to a certain degree. To further improve the detection accuracy of unseen domains, we propose iterative unsupervised domain adaptation method. Predictions of unseen cell lines with high precision enable automatic generation of training data, which is used to train the model together with parts of the previously used annotated training data. We used U-Net-based model, and three consecutive focal planes from brightfield image z-stacks. We trained the model initially with PC-3 cell line, and used LNCaP, BT-474 and 22Rv1 cell lines as target domains for domain adaptation. Highest improvement in accuracy was achieved for 22Rv1 cells. F1-score after supervised training was only 0.65, but after unsupervised domain adaptation we achieved a score of 0.84. Mean accuracy for target domains was 0.87, with mean improvement of 16 percent.

CONCLUSIONS

With our method for generalized cell detection, we can train a model that accurately detects different cell lines from brightfield images. A new cell line can be introduced to the model without a single manual annotation, and after iterative domain adaptation the model is ready to detect these cells with high accuracy.

Automated noninvasive epithelial cell counting in phase contrast microscopy images with automated parameter selection

Cell counting is commonly used to determine proliferation rates in cell cultures and for adherent cells it is often a ‘destructive’ process requiring disruption of the cell monolayer resulting in the inability to follow cell growth longitudinally. This process is time consuming and utilises significant resource. In this study a relatively inexpensive, rapid and widely applicable phase contrast microscopy‐based technique has been developed that emulates the contrast changes taking place when bright field microscope images of epithelial cell cultures are defocused. Processing of the resulting images produces an image that can be segmented using a global threshold; the number of cells is then deduced from the number of segmented regions and these cell counts can be used to generate growth curves. The parameters of this method were tuned using the discrete mereotopological relations between ground truth and processed images. Cell count accuracy was improved using linear discriminant analysis to identify spurious noise regions for removal.

The proposed cell counting technique was validated by comparing the results with a manual count of cells in images, and subsequently applied to generate growth curves for oral keratinocyte cultures supplemented with a range of concentrations of foetal calf serum. The approach developed has broad applicability and utility for researchers with standard laboratory imaging equipment.

Improved contrast microscopy: modification of bright field for urine sediment visualisation

BACKGROUND

Phase contrast microscopy is the recommended technique for urine sediment examination. Bright field microscopy does not differentiate objects with slight changes in the refractive index and hence phase contrast is a superior alternative.

METHODS

In this article, we describe a novel method to improve contrast in bright field microscopy. A strategically placed disc of specific dimensions enhances the diffraction of rays by Fresnel principle causing a shift in wavelength in the rays which are perceived as differences in contrast by the eye due to constructive and destructive interference.

RESULTS

Epithelial cells, red blood cells (RBCs), dysmorphic red blood cells, casts, bacteria and crystals are easily seen and differentiated by this technique.

CONCLUSIONS

The images obtained are similar to those obtained by phase contrast microscopy.

A Simple and Sensitive High-Content Assay for the Characterization of Antiproliferative Therapeutic Antibodies

Monoclonal antibodies (mAbs) have become a central class of therapeutic agents in particular as antiproliferative compounds. Their often complex modes of action require sensitive assays during early, functional characterization. Current cell-based proliferation assays often detect metabolites that are indicative of metabolic activity but do not directly account for cell proliferation. Measuring DNA replication by incorporation of base analogues such as 5-bromo-2'-deoxyuridine (BrdU) fills this analytical gap but was previously restricted to bulk effect characterization in enzyme-linked immunosorbent assay formats. Here, we describe a cell-based assay format for the characterization of antiproliferative mAbs regarding potency and mode of action in a single experiment. The assay makes use of single cell-based high-content-analysis (HCA) for the reliable quantification of replicating cells and DNA content via 5-ethynyl-2'-deoxyuridine (EdU) and 4',6-diamidino-2-phenylindole (DAPI), respectively, as sensitive measures of antiproliferative mAb activity. We used trastuzumab, an antiproliferative therapeutic antibody interfering with HER2 cell surface receptor-mediated growth signal transduction, and HER2-overexpressing cell lines BT474 and SKBR3 to demonstrate up to 10-fold signal-to-background (S/B) ratios for treated versus untreated cells and a shift in cell cycle profiles indicating antibody-induced cell cycle arrest. The assay is simple, cost-effective, and sensitive, providing a cell-based format for preclinical characterization of therapeutic mAbs.

Semi-quantitative monitoring of confluence of adherent mesenchymal stromal cells on calcium-phosphate granules by using widefield microscopy images

The analysis of cell confluence and proliferation is essential to design biomaterials and scaffolds to use as bone substitutes in clinical applications. Accordingly, several approaches have been proposed in the literature to estimate the area of the scaffold covered by cells. Nevertheless, most of the approaches rely on sophisticated equipment not employed for routine analyses, while the rest of them usually do not provide significant statistics about the cell distribution. This research aims at studying confluence and proliferation of mesenchymal stromal cells (MSC) adherent on OSPROLIFE(®), a commercial biomaterial in the form of granules. In particular, we propose a Computer Vision approach that can routinely be employed to monitor the surface of the single granules covered by cells because only a standard widefield fluorescent microscope is required. In order to acquire significant statistics data, we analyse wide-area images built by using MicroMos v2.0, an updated version of a previously published software specific for stitching brightfield and phase-contrast images manually acquired via a widefield microscope. In particular, MicroMos v2.0 permits to build accurate "mosaics" of fluorescent images, after correcting vignetting and photo-bleaching effects, providing a consistent representation of a sample region containing numerous granules. Then, our method allows to make automatically a statistically significant estimate of the percentage of the area of the single granules covered by cells. Finally, by analysing hundreds of granules at different time intervals we also obtained reliable data regarding cell proliferation, confirming that not only MSC adhere onto the OSPROLIFE(®) granules, but even proliferate over time.

Unidirectional P-body transport during the yeast cell cycle

P-bodies belong to a large family of RNA granules that are associated with post-transcriptional gene regulation, conserved from yeast to mammals, and influence biological processes ranging from germ cell development to neuronal plasticity. RNA granules can also transport RNAs to specific locations. Germ granules transport maternal RNAs to the embryo, and neuronal granules transport RNAs long distances to the synaptic dendrites. Here we combine microfluidic-based fluorescent microscopy of single cells and automated image analysis to follow p-body dynamics during cell division in yeast. Our results demonstrate that these highly dynamic granules undergo a unidirectional transport from the mother to the daughter cell during mitosis as well as a constrained “hovering” near the bud site half an hour before the bud is observable. Both behaviors are dependent on the Myo4p/She2p RNA transport machinery. Furthermore, single cell analysis of cell size suggests that PBs play an important role in daughter cell growth under nutrient limiting conditions.

Myoblast alignment on 2D wavy patterns: dependence on feature characteristics and cell-cell interaction

In this study, we investigate the effects of micron-scale surface patterns on the alignment of individual cells and groups of cells. Using a simple replication molding process we produce a number of micron-scale periodic wavy patterns with different pitch and depth. We observe C2C12 cells as they grow to confluence on these patterns and find that, for some geometries, cell-cell interaction leads to global alignment in a confluent culture when individual cells would not align on the same pattern. Three types of alignment behavior are thus defined: no alignment, immediate alignment, and alignment upon confluence. To further characterize this response, we introduce a non-dimensional parameter that describes the aligning power of a periodic pattern based on its geometry. The three types of alignment behavior can be distinguished by the value of the alignment parameter, and we identify values at which the transitions in alignment behavior occur. Applying this parameter to data from the current and several earlier studies reveals that the parameter successfully describes substrate aligning power over a wide range of length scales for both wavy and grooved features.

Counting unstained, confluent cells by modified bright-field microscopy

We present a very simple procedure yielding high-contrast images of adherent, confluent cells such as human neuroblastoma (SH-EP) cells by ordinary bright-field microscopy. Cells are illuminated through a color filter and a pinhole aperture placed between the condenser and the cell culture surface. Refraction by each cell body generates a sharp, bright spot when the image is defocused. The technique allows robust, automatic cell counting from a single bright-field image in a wide range of focal positions using free, readily available image-analysis tools. Contrast may be enhanced by swelling cell bodies with a brief incubation in PBS. The procedure was benchmarked against manual and automated counting of fluorescently labeled cell nuclei. Counts from day-old and freshly seeded plates were compared in a range of densities, from sparse to densely overgrown. On average, bright-field images produced the same counts as fluorescence images, with less than 5% error. This method will allow routine cell counting using a plain bright-field microscope without cell-line modification or cell staining.

Automated method for the rapid and precise estimation of adherent cell culture characteristics from phase contrast microscopy images

The quantitative determination of key adherent cell culture characteristics such as confluency, morphology, and cell density is necessary for the evaluation of experimental outcomes and to provide a suitable basis for the establishment of robust cell culture protocols. Automated processing of images acquired using phase contrast microscopy (PCM), an imaging modality widely used for the visual inspection of adherent cell cultures, could enable the non-invasive determination of these characteristics. We present an image-processing approach that accurately detects cellular objects in PCM images through a combination of local contrast thresholding and post hoc correction of halo artifacts. The method was thoroughly validated using a variety of cell lines, microscope models and imaging conditions, demonstrating consistently high segmentation performance in all cases and very short processing times (<1 s per 1,208 × 960 pixels image). Based on the high segmentation performance, it was possible to precisely determine culture confluency, cell density, and the morphology of cellular objects, demonstrating the wide applicability of our algorithm for typical microscopy image processing pipelines. Furthermore, PCM image segmentation was used to facilitate the interpretation and analysis of fluorescence microscopy data, enabling the determination of temporal and spatial expression patterns of a fluorescent reporter. We created a software toolbox (PHANTAST) that bundles all the algorithms and provides an easy to use graphical user interface. Source-code for MATLAB and ImageJ is freely available under a permissive open-source license. Biotechnol. Bioeng. 2014;111: 504–517. © 2013 Wiley Periodicals, Inc.