Dataset Information

Image-based high-throughput mapping of TGF-β-induced phosphocomplexes at a single-cell level.

ABSTRACT: Protein interactions and posttranslational modifications orchestrate cellular responses to e.g. cytokines and drugs, but it has been difficult to monitor these dynamic events in high-throughput. Here, we describe a semi-automated system for large-scale in situ proximity ligation assays (isPLA), combining isPLA in microtiter wells with automated microscopy and computer-based image analysis. Phosphorylations and interactions are digitally recorded along with subcellular morphological features. We investigated TGF-β-responsive Smad2 linker phosphorylations and complex formations over time and across millions of individual cells, and we relate these events to cell cycle progression and local cell crowding via measurements of DNA content and nuclear size of individual cells, and of their relative positions. We illustrate the suitability of this protocol to screen for drug effects using phosphatase inhibitors. Our approach expands the scope for image-based single cell analyses by combining observations of protein interactions and modifications with morphological details of individual cells at high throughput.

SUBMITTER: Lonn P

PROVIDER: S-EPMC8590043 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Image-based high-throughput mapping of TGF-β-induced phosphocomplexes at a single-cell level.

Lönn Peter P Al-Amin Rasel A RA Doulabi Ehsan Manouchehri EM Heldin Johan J Gallini Radiosa R Björkesten Johan J Oelrich Johan J Kamali-Moghaddam Masood M Landegren Ulf U

Communications biology 20211112 1

Protein interactions and posttranslational modifications orchestrate cellular responses to e.g. cytokines and drugs, but it has been difficult to monitor these dynamic events in high-throughput. Here, we describe a semi-automated system for large-scale in situ proximity ligation assays (isPLA), combining isPLA in microtiter wells with automated microscopy and computer-based image analysis. Phosphorylations and interactions are digitally recorded along with subcellular morphological features. We ...[more]

PMID: 34773084

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Project description:BACKGROUND:Image-based high throughput (HT) screening provides a rich source of information on dynamic cellular response to external perturbations. The large quantity of data generated necessitates computer-aided quality control (QC) methodologies to flag imaging and staining artifacts. Existing image- or patch-level QC methods require separate thresholds to be simultaneously tuned for each image quality metric used, and also struggle to distinguish between artifacts and valid cellular phenotypes. As a result, extensive time and effort must be spent on per-assay QC feature thresholding, and valid images and phenotypes may be discarded while image- and cell-level artifacts go undetected. RESULTS:We present a novel cell-level QC workflow built on machine learning approaches for classifying artifacts from HT image data. First, a phenotype sampler based on unlabeled clustering collects a comprehensive subset of cellular phenotypes, requiring only the inspection of a handful of images per phenotype for validity. A set of one-class support vector machines are then trained on each biologically valid image phenotype, and used to classify individual objects in each image as valid cells or artifacts. We apply this workflow to two real-world large-scale HT image datasets and observe that the ratio of artifact to total object area (ARcell) provides a single robust assessment of image quality regardless of the underlying causes of quality issues. Gating on this single intuitive metric, partially contaminated images can be salvaged and highly contaminated images can be excluded before image-level phenotype summary, enabling a more reliable characterization of cellular response dynamics. CONCLUSIONS:Our cell-level QC workflow enables identification of artificial cells created not only by staining or imaging artifacts but also by the limitations of image segmentation algorithms. The single readout ARcell that summaries the ratio of artifacts contained in each image can be used to reliably rank images by quality and more accurately determine QC cutoff thresholds. Machine learning-based cellular phenotype clustering and sampling reduces the amount of manual work required for training example collection. Our QC workflow automatically handles assay-specific phenotypic variations and generalizes to different HT image assays.

Project description:Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

Dataset Information

Image-based high-throughput mapping of TGF-β-induced phosphocomplexes at a single-cell level.

Publications

Image-based high-throughput mapping of TGF-β-induced phosphocomplexes at a single-cell level.

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