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

Linking the structure of alpha-synuclein oligmers to function in Parkinson's disease.

Misfolding and aggregation of alpha-synuclein (a-syn) are associated with a range of neurological disorders, including Parkinson's disease (PD). Fibrillar, insoluble aggregates of a-syn, known as Lewy bodies (LBs) are deposited in the substantia nigra and are a pathological hallmark of PD. a-syn is a natively unstructured protein, co-populating extended and more compact conformational forms under equilibrium. The fine balance of this equilibrium can be shifted due to changes in its environment such as alterations in metal content, ionic strength, free dopamine or others, promoting the assembly of a-syn into toxic conformations. Small, soluble oligomers preceding LB formation are thought to be causative, in vitro, different a-syn oligomers have been produced with alternate biochemical properties. Here the primary objective was to uncover the link between conformation and toxic gain of function by the use of functional assays in combination with ESI-IMS-MS. Epitope mapping procedures indicated that different a-syn oligomers have unique epitope features. Dye binding assays such as ThT and ANS fluorescence inferred that the various oligomer types differ in their amyloidogenicity and hydrophobicity. Furthermore, intracellular aggregation assays, MTT cell proliferation and Ca(ll) influx analysis in SH-SY5Y neuroblastoma cells showed that cellular effects correlated with structural features. ESI-IMS-MS spectra of the different oligomers have been acquired and allowed the conformations of the oligomer subsets to be determined. The oligomers assembled up to a hexameric form with a closed ring-like conformation. These results demonstrated that unique structural features are required for toxicity and that a subset of oligomers with characteristic structures may be pivotal in PD

3D-Cell-Annotator : an open-source active surface tool for single-cell segmentation in 3D microscopy images

aSummary: Segmentation of single cells in microscopy images is one of the major challenges in computational biology. It is the first step of most bioimage analysis tasks, and essential to create training sets for more advanced deep learning approaches. Here, we propose 3D-Cell-Annotator to solve this task using 3D active surfaces together with shape descriptors as prior information in a semi-automated fashion. The software uses the convenient 3D interface of the widely used Medical Imaging Interaction Toolkit (MITK). Results on 3D biological structures (e.g. spheroids, organoids and embryos) show that the precision of the segmentation reaches the level of a human expert.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

Cell lines and clearing approaches : a single-cell level 3D light-sheet fluorescence microscopy dataset of multicellular spheroids

Nowadays, three dimensional (3D) cell cultures are widely used in the biological laboratories and several optical clearing approaches have been proposed to visualize individual cells in the deepest layers of cancer multicellular spheroids. However, defining the most appropriate clearing approach for the different cell lines is an open issue due to the lack of a gold standard quantitative metric. In this article, we describe and share a single-cell resolution 3D image dataset of human carcinoma spheroids imaged using a light-sheet fluorescence microscope. The dataset contains 90 multicellular cancer spheroids derived from 3 cell lines (i.e. T-47D, 5-8F, and Huh-7D12) and cleared with 5 different protocols, precisely Clear(T) , Clear(T2) , CUBIC, ScaleA2, and Sucrose. To evaluate image quality and light penetration depth of the cleared 3D samples, all the spheroids have been imaged under the same experimental conditions, labelling the nuclei with the DRAQ(5) stain and using a Leica SP8 Digital LightSheet microscope. The clearing quality of this dataset was annotated by 10 independent experts and thus allows microscopy users to qualitatively compare the effects of different optical clearing protocols on different cell lines. It is also an optimal testbed to quantitatively assess different com putational metrics evaluating the image quality in the deepest layers of the spheroids. (C) 2021 The Author(s). Published by Elsevier Inc.Peer reviewe

A quantitative metric for the comparative evaluation of optical clearing protocols for 3D multicellular spheroids

3D multicellular spheroids quickly emerged as in vitro models because they represent the in vivo tumor environment better than standard 2D cell cultures. However, with current microscopy technologies, it is difficult to visualize individual cells in the deeper layers of 3D samples mainly because of limited light penetration and scattering. To overcome this problem several optical clearing methods have been proposed but defining the most appropriate clearing approach is an open issue due to the lack of a gold standard metric. Here, we propose a guideline for 3D light microscopy imaging to achieve single-cell resolution. The guideline includes a validation experiment focusing on five optical clearing protocols. We review and compare seven quality metrics which quantitatively characterize the imaging quality of spheroids. As a test environment, we have created and shared a large 3D dataset including approximately hundred fluorescently stained and optically cleared spheroids. Based on the results we introduce the use of a novel quality metric as a promising method to serve as a gold standard, applicable to compare optical clearing protocols, and decide on the most suitable one for a particular experiment. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.Peer reviewe

nucleAIzer : A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer

Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is that during training nucleAIzer automatically adapts its nucleus-style model to unseen and unlabeled data using image style transfer to automatically generate augmented training samples. This allows the model to recognize nuclei in new and different experiments efficiently without requiring expert annotations, making deep learning for nucleus segmentation fairly simple and labor free for most biological light microscopy experiments. It can also be used online, integrated into CellProfiler and freely downloaded at www.nucleaizer.org. A record of this paper's transparent peer review process is included in the Supplemental Information.Peer reviewe

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Fisheye transformation

Installation It is advised to create a virtual environment  python -m virtualenv venv source venv/bin/activate # activation on Linux venv\Scripts\activate.bat # activation on Windows You can install the required packages from the repository folder with  pip install -r requirements.txt Usage The application can be run from console:  python transform.py  PATH/TO/INPUT_IMAGES  [PATH/TO/DESTINATION]  [-f FOCAL_LENGTH]  [-a ALPHA]  [-m MAPPING_FUNCTION] Parameters PATH/TO/INPUT_IMAGES: path to the folder containing the input images for the transformation.    The folder should also include a text file called positions.csv, which contains the    center positions of the object selected for transformation. Format:   filename1,x_position1,y_position1 filename2,x_position2,y_position2 ...  PATH/TO/DESTINATION: the target folder for the transformed images. If not provided, images    will be saved to path/to/input_images/transformed FOCAL_LENGTH: the focal length of the virtual fisheye lens. If missing, a GUI will appear   for experimenting with different values. ALPHA: size of the transformed area in pixels. If missing, a GUI will appear for    experimenting with different values. MAPPING_FUNCTION: mapping function of the virtual fisheye lens.   Must be one of rectilinear, stereographic, equidistant, equisolid or orthographic.   Defaults to equidistant. Note: The FisheyeTransform class can also use a callable, which takes two parameters,   the radius of the transformed point and the focal length. This allows for virtually any   custom mapping function to be used with the transformation. </ul

Lung cancer images

The slides of lung cancer tissues were stained with hematoxylin-eosin (HE) in standard histopathological procedures. Formalin-fixed and paraffin-embedded tissue sections were cut into 4 µm thick slices, and were stained using a Tissue-Tek DRS 2000E-D2 Slide Stainer (Sakura Finetek Japan) according to the manufacturer’s instructions. Using the AxioVision SE64Rel.4.9.1.1 (Carl Zeiss Meditec AG, Germany) software, images were captured with an Axio Imager Z.1 (Carl Zeiss Meditec AG, Germany) microscope equipped with an EC Plan-NEOFLUOAR 20x/0.5NA lens. </p