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Combining multiple Parkinson's disease (PD) relevant cellular phenotypes might increase the accuracy of midbrain dopaminergic neuron (mDAN) in vitro models. We differentiated patient-derived induced pluripotent stem cells (iPSCs) with a LRRK2 G2019S mutation, isogenic control, and genetically unrelated iPSCs into mDANs. Using automated fluorescence microscopy in 384-well-plate format, we identified elevated levels of α-synuclein (αSyn) and serine 129 phosphorylation, reduced dendritic complexity, and mitochondrial dysfunction. Next, we measured additional image-based phenotypes and used machine learning (ML) to accurately classify mDANs according to their genotype. Additionally, we show that chemical compound treatments, targeting LRRK2 kinase activity or αSyn levels, are detectable when using ML classification based on multiple image-based phenotypes. We validated our approach using a second isogenic patient-derived SNCA gene triplication mDAN model which overexpresses αSyn. This phenotyping and classification strategy improves the practical exploitability of mDANs for disease modeling and the identification of novel LRRK2-associated drug targets.

Image-based profiling has emerged as a powerful technology for various steps in basic biological and pharmaceutical discovery, but the community has lacked a large, public reference set of data from chemical and genetic perturbations. Here we present data generated by the Joint Undertaking for Morphological Profiling (JUMP)-Cell Painting Consortium, a collaboration between 10 pharmaceutical companies, six supporting technology companies, and two non-profit partners. When completed, the dataset will contain images and profiles from the Cell Painting assay for over 116,750 unique compounds, over-expression of 12,602 genes, and knockout of 7,975 genes using CRISPR-Cas9, all in human osteosarcoma cells (U2OS). The dataset is estimated to be 115 TB in size and capturing 1.6 billion cells and their single-cell profiles. File quality control and upload is underway and will be completed over the coming months at the Cell Painting Gallery:https://registry.opendata.aws/cellpainting-gallery. A portal to visualize a subset of the data is available athttps://phenaid.ardigen.com/jumpcpexplorer/.

Lung carcinoids are neuroendocrine tumors representing 1 to 2% of lung cancers. This study outlines the case of a patient with a metastatic lung atypical carcinoid who presented with a pleural effusion and progression of liver metastases after developing resistance to conventional treatments. Personalized functional profiling (PFP), i.e. drug screening, was performed in ex-vivo spheroids obtained from the patient’s liver metastasis to identify potential therapeutic options. The drug screening results revealed cediranib, an antiangiogenic drug, as a hit drug for this patient, from a library of 66 Food and Drug Administration (FDA)-approved and investigational drugs. Based on the PFP results and the reported evidence of clinical efficacy of bevacizumab and capecitabine combination in gastro-intestinal neuroendocrine tumors, this combination was given to the patient. Four months later, the pleural effusion and pleura carcinosis regressed and the liver metastasis did not progress. The patient experienced 2 years of a stable disease under the PFP-guided personalized treatment.

An obstacle to effective uniform treatment of glioblastoma, especially at recurrence, is genetic and cellular intertumoral heterogeneity. Hence, personalized strategies are necessary, as are means to stratify potential targeted therapies in a clinically relevant timeframe. Functional profiling of drug candidates against patient-derived glioblastoma organoids (PD-GBO) holds promise as an empirical method to preclinically discover potentially effective treatments of individual tumors. Here, we describe our establishment of a PD-GBO-based functional profiling platform and the results of its application to four patient tumors. We show that our PD-GBO model system preserves key features of individual patient glioblastomas in vivo. As proof of concept, we tested a panel of 41 FDA-approved drugs and were able to identify potential treatment options for three out of four patients; the turnaround from tumor resection to discovery of treatment option was 13, 14, and 15 days, respectively. These results demonstrate that this approach is a complement and, potentially, an alternative to current molecular profiling efforts in the pursuit of effective personalized treatment discovery in a clinically relevant time period. Furthermore, these results warrant the use of PD-GBO platforms for preclinical identification of new drugs against defined morphological glioblastoma features.

SummaryCombining multiple Parkinson’s disease (PD) relevant cellular phenotypes might increase the accuracy of midbrain dopaminergic (mDA) in vitro models. We differentiated patient-derived induced pluripotent stem cells (iPSCs) with a LRRK2 G2019S mutation, isogenic control and genetically unrelated iPSCs into mDA neurons. Using automated fluorescence microscopy in 384-well plate format, we identified elevated levels of α-synuclein and Serine 129 phosphorylation (pS129), reduced dendritic complexity, and mitochondrial dysfunction. Next, we measured additional image-based phenotypes and used machine learning (ML) to accurately classify mDA neurons according to their genotype. Additionally, we show that chemical compound treatments, targeting LRRK2 kinase activity or α-synuclein levels, are detectable when using ML classification based on multiple image-based phenotypes. We validated our approach using a second isogenic patient derived SNCA gene triplication mDA neuronal model. This phenotyping and classification strategy improves the exploitability of mDA neurons for disease modelling and the identification of novel PD drug targets.

Lung carcinoids are neuroendocrine tumors representing 1 to 2% of lung cancers. This study outlines the case of a patient with a metastatic lung atypical carcinoid who presented with a pleural effusion and progression of liver metastases after developing resistance to conventional treatments. Personalized functional profiling (PFP), i.e. drug screening, was performed in

Current research suggests therapy-induced senescence (TIS) of cancer cells characterized by distinct morphological and biochemical phenotypic changes represent a novel functional target that may enhance the effectiveness of cancer therapy. In order to identify novel small-molecule inducers of cellular senescence and determine the potential to be used for the treatment of melanoma, a new method of high-throughput screening (HTS) and high-contents screening (HCS) based on the detection of morphological changes was designed. This image-based and whole cell-based technology was applied to screen and select a novel class of antiproliferative agents on cancer cells, 4H-chromeno[2,3-d]pyrimidin-4-one derivatives, which induced senescence-like phenotypic changes in human melanoma A375 cells without serious cytotoxicity against normal cells. To evaluate structure-activity relationship (SAR) study of 4H-chromeno[2,3-d]pyrimidin-4-one scaffold starting from hit 3, a focused library containing diversely modified analogues was constructed and which led to the identification of 38, a novel compound to have remarkable anti-melanoma activity in vitro with good metabolic stability.

More than 100 copper/zinc superoxide dismutase 1 (SOD1) genetic mutations have been characterized. These mutations lead to the death of motor neurons in ALS. In its native form, the SOD1 protein is expressed as a homodimer in the cytosol. In vitro studies have shown that SOD1 mutations impair the dimerization kinetics of the protein, and in vivo studies have shown that SOD1 forms aggregates in patients with familial forms of ALS. In this study, we analyzed WT SOD1 and 9 mutant (mt) forms of the protein by non-invasive fluorescence techniques. Using microscopic techniques such as fluorescence resonance energy transfer, fluorescence complementation, image-based quantification, and fluorescence correlation spectroscopy, we studied SOD1 dimerization, oligomerization, and aggregation. Our results indicate that SOD1 mutations lead to an impairment in SOD1 dimerization and, subsequently, affect protein aggregation. We also show that SOD1 WT and mt proteins can dimerize. However, aggregates are predominantly composed of SOD1 mt proteins.

Motivation: High-throughput screening (HTS) is an important method in drug discovery in which the activities of a large number of candidate chemicals or genetic materials are rapidly evaluated. Data are usually obtained by measurements on samples in microwell plates and are often subjected to artefacts that can bias the result selection. We report here a novel edge effect correction algorithm suitable for RNA interference (RNAi) screening, because its normalization does not rely on the entire dataset and takes into account the specificities of such a screening process. The proposed method is able to estimate the edge effects for each assay plate individually using the data from a single control column based on diffusion model, and thus targeting a specific but recurrent well-known HTS artefact. This method was first developed and validated using control plates and was then applied to the correction of experimental data generated during a genome-wide siRNA screen aimed at studying HIV–host interactions. The proposed algorithm was able to correct the edge effect biasing the control data and thus improve assay quality and, consequently, the hit-selection step. Contact: dorvalt@ip-korea.org; jean-philippe.carralot@roche.com Supplementary information: Supplementary data are available at Bioinformatics online.

Our objective was to determine whether lipocalin-2 (Lcn2) regulates cardiomyocyte apoptosis, the mechanisms involved, and the functional significance. Emerging evidence suggests that Lcn2 is a proinflammatory adipokine associated with insulin resistance and obesity-related complications, such as heart failure. Here, we used both primary neonatal rat cardiomyocytes and H9c2 cells and demonstrated for the first time that Lcn2 directly induced cardiomyocyte apoptosis, an important component of cardiac remodeling leading to heart failure. This was shown by detection of DNA fragmentation using TUNEL assay, phosphatidylserine exposure using flow cytometry to detect annexin V-positive cells, caspase-3 activity using enzymatic assay and immunofluorescence, and Western blotting for the detection of cleaved caspase-3. We also observed that Lcn2 caused translocation of the proapoptotic protein Bax to mitochondria and disruption of mitochondrial membrane potential. Using transient transfection of GFP-Bax, we confirmed that Lcn2 induced co-localization of Bax with MitoTracker(R) dye. Importantly, we used the fluorescent probe Phen Green SK to demonstrate an increase in intracellular iron in response to Lcn2, and depleting intracellular iron using an iron chelator prevented Lcn2-induced cardiomyocyte apoptosis. Administration of recombinant Lcn2 to mice for 14 days increased cardiomyocyte apoptosis as well as an acute inflammatory response with compensatory changes in cardiac functional parameters. In conclusion, Lcn2-induced cardiomyocyte apoptosis is of physiological significance and occurs via a mechanism involving elevated intracellular iron levels and Bax translocation., link_to_OA_fulltext

Advances in automated imaging microscopy allow fast acquisitions of multidimensional biological samples. Those microscopes open new possibilities for analyzing subcellular structures and spatial cellular arrangements. In this article, the authors describe a 3D image analysis framework adapted to medium-throughput screening. Upon adaptive and regularized segmentation, followed by precise 3D reconstruction, they achieve automatic quantification of numerous relevant 3D descriptors related to the shape, texture, and fluorescence intensity of multiple stained subcellular structures. A global analysis of the 3D reconstructed scene shows additional possibilities to quantify the relative position of organelles. Implementing this methodology, the authors analyzed the subcellular reorganization of the nucleus, the Golgi apparatus, and the centrioles occurring during the cell cycle. In addition, they quantified the effect of a genetic mutation associated with the early onset primary dystonia on the redistribution of torsinA from the bulk endoplasmic reticulum to the perinuclear space of the nuclear envelope. They show that their method enables the classification of various translocation levels of torsinA and opens the possibility for compound-based screening campaigns restoring the normal torsinA phenotype. (Journal of Biomolecular Screening 2010:847-857)

International audience; In this paper we present a new framework for correcting partially out-of-focus biological images. To evaluate depth variation , our method extracts similar objects of interest over the image. The depth is based on a defocus value computation for each salient point. We use an iterative algorithm to convert each defocus value into a Gaussian standard deviation. These measures are then interpolated over the whole image using thin plate spline transform, providing a regularized map. This array provides, for every spatial position, an evaluation of the standard deviation used in the final step for an inhomogeneous deconvolution based on the Richardson-Lucy algorithm. Experiments on real and simulated images validate the accuracy of such a method compare to traditional shift invariant decon-volution approaches.

Ultrasound images are corrupted by a multiplicative noise, the speckle, which makes high level analysis difficult. Within each resolution cell a number of elementary scatterers reflects the incident wave towards the sensor. The backscattered coherent waves with different phases undergo a constructive or a destructive interference in a random manner. This paper proposes a method of restoration based on variational principles applied to wavelet coefficients. The idea of this process is to avoid the various difficulties caused by wavelet coefficients thresholding, substituting it by a total variation-based technique. The method is based on orthogonal wavelets and uses the conventional separable two-dimensional discrete wavelet transform (DWT) scheme. The coefficients of the different levels are filtered by the total variation algorithm. Nevertheless, in order to comply with ultrasound images statistics, we have adapted the constraints commonly applied to Euler-Lagrange equations. After stating first results on synthetic image, we will validate our model on real images.