Algorithm for the precise detection of single and cluster cells in microfluidic applications

Computer vision meets microfluidics: a label-free method for high-throughput cell analysis

In this paper, we review the integration of microfluidic chips and computer vision, which has great potential to advance research in the life sciences and biology, particularly in the analysis of cell imaging data. Microfluidic chips enable the generation of large amounts of visual data at the single-cell level, while computer vision techniques can rapidly process and analyze these data to extract valuable information about cellular health and function. One of the key advantages of this integrative approach is that it allows for noninvasive and low-damage cellular characterization, which is important for studying delicate or fragile microbial cells. The use of microfluidic chips provides a highly controlled environment for cell growth and manipulation, minimizes experimental variability and improves the accuracy of data analysis. Computer vision can be used to recognize and analyze target species within heterogeneous microbial populations, which is important for understanding the physiological status of cells in complex biological systems. As hardware and artificial intelligence algorithms continue to improve, computer vision is expected to become an increasingly powerful tool for in situ cell analysis. The use of microelectromechanical devices in combination with microfluidic chips and computer vision could enable the development of label-free, automatic, low-cost, and fast cellular information recognition and the high-throughput analysis of cellular responses to different compounds, for broad applications in fields such as drug discovery, diagnostics, and personalized medicine.

Variable inter and intraspecies alkaline phosphatase activity within single cells of revived dinoflagellates

Adaptation of cell populations to environmental changes is mediated by phenotypic variability at the single-cell level. Enzyme activity is a key factor in cell phenotype and the expression of the alkaline phosphatase activity (APA) is a fundamental phytoplankton strategy for maintaining growth under phosphate-limited conditions. Our aim was to compare the APA among cells and species revived from sediments of the Bay of Brest (Brittany, France), corresponding to a pre-eutrophication period (1940's) and a beginning of a post-eutrophication period (1990's) during which phosphate concentrations have undergone substantial variations. Both toxic marine dinoflagellate Alexandrium minutum and the non-toxic dinoflagellate Scrippsiella acuminata were revived from ancient sediments. Using microfluidics, we measured the kinetics of APA at the single-cell level. Our results indicate that all S. acuminata strains had significantly higher APA than A. minutum strains. For both species, the APA in the 1990's decade was significantly lower than in the 1940's. For the first time, our results reveal both inter and intraspecific variabilities of dinoflagellate APA and suggest that, at a half-century timescale, two different species of dinoflagellate may have undergone similar adaptative evolution to face environmental changes and acquire ecological advantages.

Improved efficiency of urine cell image segmentation using droplet microfluidics technology

Recent advances in the recognition of biological samples using machine vision have made this technology increasingly important in research and detection. Image segmentation is an important step in this process. This study focuses on how to reduce the interference factors such as the overlap between different types (or within the same type) of urine cells according to microfluidics and improve the machine vision segmentation accuracy for cell images. In this study, we demonstrate that the platform can realize this hypothesis using urine cell image segmentation as an example application. We first discuss the reported urine cell droplet microfluidic chip system, which can realize the test conditions in which urine cells are encapsulated in the droplet and isolated from salt crystallization and/or bacteria and other urine-formed elements. Then, based on the analysis conditions set in the aforementioned experiment, the proportions of red blood cells, white blood cells, and squamous epithelial cells covered by various formed elements in the total urine cells in the same urine sample are measured. We simultaneously analyze the percentage of urine cells covered by salt crystallization and the incidence of overlapping between urine cells. Finally, the Otsu algorithm is used to segment the urine cell images encapsulated by the droplet and the urine cell images not encapsulated by the droplet, and the Dice, Jaccard, precision, and recall values are calculated. The results suggest that the method of encapsulating single cells based on droplets can improve the image segmentation effect without optimizing the algorithm.

AR-V7 in circulating tumor cells cluster as a predictive biomarker of abiraterone acetate and enzalutamide treatment in castration-resistant prostate cancer patients

OBJECTIVE

We examined whether androgen receptor splice variant 7 (AR-V7) in circulating tumor cell(CTC)clusters can be used to predict survival in patients with bone metastatic castration resistant-prostate cancer (mCRPC) treated with abiraterone or enzalutamide.

METHODS

We retrospectively enrolled 98 patients with CRPC on abiraterone or enzalutamide, and investigated the prognostic value of CTC cluster detection (+ v -) and AR-V7 detection (+ v -) using a CTC cluster detection - based AR-V7 mRNA assay. We examined ≤50% prostate-specific antigen (PSA) responses, PSA progression-free survival (PSA-PFS), clinical and radiological progression-free survival (radiologic PSF), and overall survival (OS). We then assessed whether AR-V7 expression in CTC clusters identified after On-chip multi-imaging flow cytometry was related to disease progression and survival after first-line systemic therapy.

RESULTS

All abiraterone-treated or enzalutamide-treated patients received prior docetaxel. The median follow-up was 20.7 (range: 3.0-37.0) months in the abiraterone and enzalutamide cohorts, respectively. Forty-nine of the 98 men (50.0%) were CTC cluster (-), 23 of the 98 men (23.5%) were CTC cluster(+)/AR-V7(-), and 26 of the 98 men (26.5%) were CTC cluster(+)/AR-V7(+). CTC cluster(+)/AR-V7(+) patients were more likely to have EOD ≥3 at diagnosis (P = 0.003), pain (P = 0.023), higher alkaline phosphatase levels (P < 0.001), and visceral metastases (P < 0.001). On multivariable analysis, pretherapy CTC cluster(+), CTC cluster(+)/AR-V7(-), and ALP >UNL were independently associated with a poor PSA-PFS, radiographic PFS, and OS in abiraterone-treated patients and enzalutamide-treated patients.

CONCLUSION

The CTC clusters and AR-V7-positive CTC clusters detected were important for assessing the response to abiraterone or enzalutamide therapy and for predicting disease outcome.

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.