Fisheye transformation enhances deep-learning-based single-cell phenotyping by including cellular microenvironment

Incorporating information about the surroundings can have a significant impact on successfully determining the class of an object. This is of particular interest when determining the phenotypes of cells, for example, in the context of high-throughput screens. We hypothesized that an ideal approach would consider the fully featured view of the cell of interest, include its neighboring microenvironment, and give lesser weight to cells that are far from the cell of interest. To satisfy these criteria, we present an approach with a transformation similar to those characteristic of fisheye cameras. Using this transformation with proper settings, we could significantly increase the accuracy of single-cell phenotyping, both in the case of cell culture and tissue -based microscopy images, and we present improved results on a dataset containing images of wild animals.Peer reviewe

Environmental properties of cells improve machine learning-based phenotype recognition accuracy

To answer major questions of cell biology, it is often essential to understand the complex phenotypic composition of cellular systems precisely. Modern automated microscopes produce vast amounts of images routinely, making manual analysis nearly impossible. Due to their efficiency, machine learningbased analysis software have become essential tools to perform single-cell-level phenotypic analysis of large imaging datasets. However, an important limitation of such methods is that they do not use the information gained from the cellular micro-and macroenvironment: the algorithmic decision is based solely on the local properties of the cell of interest. Here, we present how various features from the surrounding environment contribute to identifying a cell and how such additional information can improve single-cell-level phenotypic image analysis. The proposed methodology was tested for different sizes of Euclidean and nearest neighbour-based cellular environments both on tissue sections and cell cultures. Our experimental data verify that the surrounding area of a cell largely determines its entity. This effect was found to be especially strong for established tissues, while it was somewhat weaker in the case of cell cultures. Our analysis shows that combining local cellular features with the properties of the cell's neighbourhood significantly improves the accuracy of machine learning-based phenotyping.Peer reviewe

The Combination of Single-Cell and Next-Generation Sequencing Can Reveal Mosaicism for BRCA2 Mutations and the Fine Molecular Details of Tumorigenesis

Simple Summary Germline and somatic BRCA1/2 mutations may define therapeutic targets and refine cancer treatment options. However, routine BRCA diagnostic approaches cannot reveal the exact time and origin of BRCA1/2 mutation formation, and thus, the fine details of their contribution to tumor progression remain less clear. We established a diagnostic pipeline using high-resolution microscopy and laser microcapture microscopy to test for BRCA1/2 mutations in tumors at the single-cell level, followed by deep next-generation sequencing of various tissues from the patient. To demonstrate the power of our approach, here we present a detailed analysis of an ovarian cancer patient, in which we describe constitutional somatic mosaicism of a BRCA2 mutation. Characterization of the mosaic mutation at the single-cell level contributes to a better understanding of BRCA mutation formation and supports the concept that the combination of single-cell and next-generation sequencing methods is advantageous over traditional mutational analysis methods. Germline mutations in the BRCA1 and BRCA2 genes are responsible for hereditary breast and ovarian cancer syndrome. Germline and somatic BRCA1/2 mutations may define therapeutic targets and refine cancer treatment options. However, routine BRCA diagnostic approaches cannot reveal the exact time and origin of BRCA1/2 mutation formation, and thus, the fine details of their contribution to tumor progression remain less clear. Here, we establish a diagnostic pipeline using high-resolution microscopy and laser microcapture microscopy to test for BRCA1/2 mutations in the tumor at the single-cell level, followed by deep next-generation sequencing of various tissues from the patient. To demonstrate the power of our approach, here, we describe a detailed single-cell-level analysis of an ovarian cancer patient we found to exhibit constitutional somatic mosaicism of a pathogenic BRCA2 mutation. Employing next-generation sequencing, BRCA2 c.7795G>T, p.(Glu2599Ter) was detected in 78% of reads in DNA extracted from ovarian cancer tissue and 25% of reads in DNA derived from peripheral blood, which differs significantly from the expected 50% of a hereditary mutation. The BRCA2 mutation was subsequently observed at 17-20% levels in the normal ovarian and buccal tissue of the patient. Together, our findings suggest that this mutation occurred early in embryonic development. Characterization of the mosaic mutation at the single-cell level contributes to a better understanding of BRCA mutation formation and supports the concept that the combination of single-cell and next-generation sequencing methods is advantageous over traditional mutational analysis methods. This study is the first to characterize constitutional mosaicism down to the single-cell level, and it demonstrates that BRCA2 mosaicism occurring early during embryogenesis can drive tumorigenesis in ovarian cancer.Peer reviewe

Correlation between fibroblast growth factor receptor mutation, programmed death ligand-1 expression and survival in urinary bladder cancer based on real-world data

Background: Programmed cell death (PD)-1/PD-ligand 1 (PD-L1) inhibitors have made a breakthrough in the therapy of advanced urothelial bladder cancer (UBC). The impact of Fibroblast Growth Factor Receptor 3 (FGFR3) mutation on the effectiveness of PD-L1 treatment remains still unclear. Objective: Our study aimed to investigate the frequency of FGFR mutations at different tumor stages, and their relation to PD-L1 status and survival.Methods: 310 patients with urothelial bladder cancer and subsequent radical cystectomy were included in a retrospective study over a 10-year study period at the University of Szeged, Hungary. FGFR3 mutations from the most infiltrative areas of the tumor were analyzed by targeted next-generation sequencing and PD-L1 (28-8 DAKO) tests (tumor positive score -TPS and combined positives score–CPS). In T0 cases FGFR3 mutations were analyzed from the earlier resection samples. Survival and oncological treatment data were collected from the National Health Insurance Fund (NHIF). Neoadjuvant, adjuvant and palliative conventional chemotherapies were allowed; immunotherapies were not. The relationship between the covariates was tested using chi-square tests, and survival analysis was performed using the Kaplan-Meier model and Cox proportional hazards regression.Results: PD-L1 and FGFR could be tested successfully in 215 of the 310 UBC samples [pT0cyst 19 (8.8%); St.0-I 43 (20%); St.II 41 (19%); St.III-IV 112 (52%)]. Significant pairwise dependency was found between tumor stage, FGFR3 mutation status and PD-L1 expression (p < 0.01). Samples with FGFR mutation were more common in less advanced stages and were also less likely to demonstrate PD-L1 expression. The effect of all investigated factors on survival was found to correlate with tumor stage.Conclusion: FGFR alteration frequency varied between the different stages of cancer. Higher positivity rates were observed at early stages, but lower levels of PD-L1 expression were detected in patients with FGFR mutations across at all stages of the disease

Intelligent image-based in situ single-cell isolation

Quantifying heterogeneities within cell populations is important for many fields including cancer research and neurobiology; however, techniques to isolate individual cells are limited. Here, we describe a high-throughput, non-disruptive, and cost-effective isolation method that is capable of capturing individually targeted cells using widely available techniques. Using high-resolution microscopy, laser microcapture microscopy, image analysis, and machine learning, our technology enables scalable molecular genetic analysis of single cells, targetable by morphology or location within the sample.Peer reviewe

A versatile transposon-based technology to generate loss- and gain-of-function phenotypes in the mouse liver

Background Understanding the contribution of gene function in distinct organ systems to the pathogenesis of human diseases in biomedical research requires modifying gene expression through the generation of gain- and loss-of-function phenotypes in model organisms, for instance, the mouse. However, methods to modify both germline and somatic genomes have important limitations that prevent easy, strong, and stable expression of transgenes. For instance, while the liver is remarkably easy to target, nucleic acids introduced to modify the genome of hepatocytes are rapidly lost, or the transgene expression they mediate becomes inhibited due to the action of effector pathways for the elimination of exogenous DNA. Novel methods are required to overcome these challenges, and here we develop a somatic gene delivery technology enabling long-lasting high-level transgene expression in the entire hepatocyte population of mice. Results We exploit the fumarylacetoacetate hydrolase (Fah) gene correction-induced regeneration in Fah-deficient livers, to demonstrate that such approach stabilizes luciferase expression more than 5000-fold above the level detected in WT animals, following plasmid DNA introduction complemented by transposon-mediated chromosomal gene transfer. Building on this advancement, we created a versatile technology platform for performing gene function analysis in vivo in the mouse liver. Our technology allows the tag-free expression of proteins of interest and silencing of any arbitrary gene in the mouse genome. This was achieved by applying the HADHA/B endogenous bidirectional promoter capable of driving well-balanced bidirectional expression and by optimizing in vivo intronic artificial microRNA-based gene silencing. We demonstrated the particular usefulness of the technology in cancer research by creating a p53-silenced and hRas G12V-overexpressing tumor model. Conclusions We developed a versatile technology platform for in vivo somatic genome editing in the mouse liver, which meets multiple requirements for long-lasting high-level transgene expression. We believe that this technology will contribute to the development of a more accurate new generation of tools for gene function analysis in mice.Peer reviewe