A stochastic mathematical model of 4D tumour spheroids with real-time fluorescent cell cycle labelling

In vitro tumour spheroid experiments have been used to study avascular tumour growth and drug design for the last 50 years. Unlike simpler two-dimensional cell cultures, tumour spheroids exhibit heterogeneity within the growing population of cells that is thought to be related to spatial and temporal differences in nutrient availability. The recent development of real-time fluorescent cell cycle imaging allows us to identify the position and cell cycle status of individual cells within the growing population, giving rise to the notion of a four-dimensional (4D) tumour spheroid. In this work we develop the first stochastic individual-based model (IBM) of a 4D tumour spheroid and show that IBM simulation data qualitatively and quantitatively compare very well with experimental data from a suite of 4D tumour spheroid experiments performed with a primary human melanoma cell line. The IBM provides quantitative information about nutrient availability within the spheroid, which is important because it is very difficult to measure these data in standard tumour spheroid experiments. Software required to implement the IBM is available on GitHub.

Dedifferentiation of Human Adipocytes After Fat Transplantation

Background Fat transplantation is a common method employed to treat soft-tissue defects. The dedifferentiation of mature adipocytes has been well documented, but whether it occurs after fat transplantation remains unclear. Objectives The major purpose of this project was to investigate the dedifferentiation of mature adipocytes after fat transplantation. Methods Human lipoaspirate tissue was obtained from 6 female patients who underwent esthetic liposuction. Mature adipocytes were extracted and labeled with PKH26, mixed with lipoaspirate, and injected into nude mice. In addition, PKH26+ adipocytes were subjected to a ceiling culture. Grafted fat was harvested from nude mice, and stromal vascular fragment cells were isolated. The immunophenotype of PKH26+ cells was detected by flow cytometry analysis at 2 days and 1 week. The PKH26+ cells were sorted and counted at 2 and 4 weeks to verify their proliferation and multilineage differentiation abilities. Results Two days after transplantation, almost no PKH26+ cells were found in the stromal vascular fragment cells. The PKH26+ cells found 1 week after transplantation showed a positive expression of cluster of differentiation (CD) 90 (CD90) and CD105 and a negative expression of CD45. This indicates that the labeled adipocytes were dedifferentiated. Its pluripotency was further demonstrated by fluorescent cell sorting and differentiation culture in vitro. In addition, the number of live PKH26+ cells at week 4 [(6.83 ± 1.67) × 104] was similar with that at week 2 [(7.11 ± 1.82) × 104]. Conclusions Human mature adipocytes can dedifferentiate into stem cell-like cells in vivo after fat transplantation.

From Toxicity to Selectivity: Coculture of the Fluorescent Tumor and Non-Tumor Lung Cells and High-Throughput Screening of Anticancer Compounds

For the search of anticancer compounds in modern large chemical libraries, new approaches are of great importance. Cocultivation of the cells of tumor and non-tumor etiology may reveal specific action of chemicals on cancer cells and also take into account some effects of the tumor cell’s microenvironment. The fluorescent cell cocultivation test (FCCT) has been developed for screening of substances that are selectively cytotoxic on cancerous cells. It is based on the mixed culture of lung carcinoma cells A549’_EGFP and noncancerous fibroblasts of lung VA13_Kat, expressing different fluorescent proteins. Analysis of the cells was performed with the high-resolution scanner to increase the detection rate. The combination of cocultivation of cells with scanning of fluorescence reduces the experimental protocol to three steps: cells seeding, addition of the substance, and signal detection. The FCCT analysis does not disturb the cells and is compatible with other cell-targeted assays. The suggested method has been adapted for a high-throughput format and applied for screening of 2,491 compounds. Three compounds were revealed to be reproducibly selective in the FCCT although they were invisible in cytotoxicity tests in individual lines. Six structurally diverse indole, coumarin, sulfonylthiazol, and rifampicin derivatives were found and confirmed with an independent assay (MTT) to be selectively cytotoxic to cancer cells in the studied model.

Combining Fluorescent Cell Sorting and Single B Cell Amplification to Screen the Monoclonal Antibody Gene against Human Glypican-1 in Pancreatic Cancer

In this report, one novel method has been developed to screen the monoclonal antibody against human pancreatic cancer biomarker glypican-1 (GPC1) through the combination of fluorescent cell sorting and single B cell amplification. GPC1-positive B cells were sorted out from the peripheral blood mononuclear cells (PBMCs) by fluorescent cell sorting after the GPC1 immunization to the New Zealand white rabbit. Then, total RNA was extracted and reversely transcribed into cDNA, which was used as the template, and the variable region sequences of both heavy and light chains were amplified from the same B cell. Next, their recombinant antibody was expressed and purified from the human 293T cell after the antibody gene amplification and expression vector construction. The enzyme-linked immunosorbent assay (ELISA) and flow cytometry assays were used to determine the antibody affinity. The antibody named GPC-12 that we screened and obtained was proven to have natural heavy-light chain pairing information, and it was highly specific to the GPC1 antigen, and the affinity could reach 1 × 10−7 M. Overall, an effective and novel method has been successfully developed to screen the antibody by combining the fluorescent cell sorting and single-cell amplifying technologies, which was proved to be workable in our setting.

Estimating parameters of a stochastic cell invasion model with fluorescent cell cycle labelling using approximate Bayesian computation

We develop a parameter estimation method based on approximate Bayesian computation (ABC) for a stochastic cell invasion model using fluorescent cell cycle labelling with proliferation, migration and crowding effects. Previously, inference has been performed on a deterministic version of the model fitted to cell density data, and not all parameters were identifiable. Considering the stochastic model allows us to harness more features of experimental data, including cell trajectories and cell count data, which we show overcomes the parameter identifiability problem. We demonstrate that, while difficult to collect, cell trajectory data can provide more information about the parameters of the cell invasion model. To handle the intractability of the likelihood function of the stochastic model, we use an efficient ABC algorithm based on sequential Monte Carlo. Rcpp and MATLAB implementations of the simulation model and ABC algorithm used in this study are available at https://github.com/michaelcarr-stats/FUCCI .

Epidemiology of Blackleg Disease in Ethiopia

Blackleg is an infectious bacterial disease of cattle and rarely of other ruminants. This bacteria is caused by Clostridium chauvoei which is an anaerobic, gram positive, motile, rod-shaped bacillus bacterium and persists in the soil as resistant spores. Blackleg is an acute or subacute but chronic disease may occur. It occurs most frequently in animals 6-24 months of age and the disease mainly affects none vaccinated as well as animals in good nutritional condition. It produces persistent spores when conditions are not ideal and spores are highly resistant to environmental factors and disinfectants. Infected ruminants do not directly transmit the disease to other animals. The bacteria enter the body through the alimentary mucosa after ingestion of contaminated feed. Secretion of cytolytic toxins that cause necrosis of vascular endothelia .The toxins are absorbed into the animal’s bloodstream which makes the animal acutely sick and causes rapid death. Economic importance due to blackleg is loss of animals, milk production and draft oxen, and cost for treatment and vaccination. Fatality rate of blackleg in fully susceptible populations approaches 100%. Clinical Signs include lethargy anorexia, reluctance to move lameness and recumbence. When superficial muscles are affected, swelling and crepitus are evident. Cattle found dead of blackleg are lying on the side with the affected hind limb stands out stiffly, bloating and putrefaction occur quickly and bloodstained from exudates, nostrils and anus. The disease can be diagnosed using laboratory diagnosis, Immune Fluorescent, Cell Culture and PCR. Control and prevention relies mainly on vaccination.

Neutrophil and CD4+ milk cell count related to natural incidence of mastitis in Jersey cattle

This Research Communication describes the relation between somatic cells and microbial content in milk from Jersey cattle. Milk samples were classified in groups: healthy, dirty and mastitic (from Staphylococcus spp., Escherichia coli, Coliforms). The somatic cells in each of those groups were analysed by two methods – flow cytometric and automatic fluorescent cell counting. Those methods were compared. Total somatic cell count (SCC), neutrophil count, and lymphocytes with cluster of differentiation 4 (CD4+cells) were determined. There was a positive relationship between microbes and somatic cells. It was noticed that the neutrophil count was generally increased together with SCC, whilst the CD4+ cell count was higher in healthy milk samples (about 8%) compared to mastitic ones (about 3%). Lower number of CD4+ cells (from 1 to 4%) was determined in samples positive for Staphylococcus spp. but with lower SCC (from 2.7 to 4.0 × 105 cells/ml). Also, the number of CD4+ cells in Staphylococcus spp.-positive samples increased (to 4.8%) together with higher SCC, something that was not observed in the other mastitic samples. Knowledge of those relations could be useful for veterinary medical tests in the initial phase of inflammation.

Estimating parameters of a stochastic cell invasion model with fluorescent cell cycle labelling using Approximate Bayesian Computation

We develop a parameter estimation method based on approximate Bayesian computation (ABC) for a stochastic cell invasion model using fluorescent cell cycle labeling with proliferation, migration, and crowding effects. Previously, inference has been performed on a deterministic version of the model fitted to cell density data, and not all the parameters were identifiable. Considering the stochastic model allows us to harness more features of experimental data, including cell trajectories and cell count data, which we show overcomes the parameter identifiability problem. We demonstrate that, whilst difficult to collect, cell trajectory data can provide more information about the parameters of the cell invasion model. To handle the intractability of the likelihood function of the stochastic model, we use an efficient ABC algorithm based on sequential Monte Carlo. Rcpp and MATLAB implementations of the simulation model and ABC algorithm used in this study are available athttps://github.com/michaelcarr-stats/FUCCI.

Automatic image annotation for fluorescent cell nuclei segmentation

Dataset annotation is a time and labor-intensive task and an integral requirement for training and testing deep learning models. The segmentation of images in life science microscopy requires annotated image datasets for object detection tasks such as instance segmentation. Although the amount of annotated image data has been steadily reduced due to methods such as data augmentation, the process of manual or semi-automated data annotation is the most labor and cost intensive task in the process of cell nuclei segmentation with deep neural networks. In this work we propose a system to fully automate the annotation process of a custom fluorescent cell nuclei image dataset. By that we are able to reduce nuclei labelling time by up to 99.5%. The output of our system provides high quality training data for machine learning applications to identify the position of cell nuclei in microscopy images. Our experiments have shown that the automatically annotated dataset provides coequal segmentation performance compared to manual data annotation. In addition, we show that our system enables a single workflow from raw data input to desired nuclei segmentation and tracking results without relying on pre-trained models or third-party training datasets for neural networks.