The research focuses on the SHANK3 gene, which is crucial for the proper development of neural connections. SHANK3 deficiency is a common factor in PMDS, leading to a range of developmental abnormalities. Our study detailed the developmental and synaptic disruptions caused by SHANK3 mutations and used a chemical genomics approach to identify potentially causative cellular pathways.

A key aspect of the research is the identification of small molecular compounds that can address the hyperdifferentiation phenotype seen in SHANK3 deficiency. Among these, one molecule has been highlighted for its ability to positively influence synapse formation and correct neurodevelopmental pathways potentially via influencing cellular actine dynamics.

Ksilink researchers will continue to explore the cellular biology of SHANK3-deficient cells. The identified compounds together with the used cell lines provide a model for understanding the effects of SHANK3 deficiency and hold potential for future development into therapeutic interventions.

Ksilink remains dedicated to advancing the understanding of PMDS and contributing to the broader field of drug discovery. Through continued research and collaboration, we aim to provide valuable models and tools that can aid in the development of effective treatments for PMDS and related disorders.

Our scientific paper can be accessed here: https://rdcu.be/dKnYt

For further details on our research, please visit our website or reach out to us.

L’article Ksilink Advances PMDS Research and Disease Modelling est apparu en premier sur Ksilink.

Lamiable, A., Champetier, T., Leonardi, F. et al. Revealing invisible cell phenotypes with conditional generative modeling. Nat Commun 14, 6386 (2023).

Biological sciences, drug discovery and medicine rely heavily on cell phenotype perturbation and microscope observation. However, most cellular phenotypic changes are subtle and thus hidden from us by natural cell variability: two cells in the same condition already look different. In this study, we show that conditional generative models can be used to transform an image of cells from any one condition to another, thus canceling cell variability. We visually and quantitatively validate that the principle of synthetic cell perturbation works on discernible cases. We then illustrate its effectiveness in displaying otherwise invisible cell phenotypes triggered by blood cells under parasite infection, or by the presence of a disease-causing pathological mutation in differentiated neurons derived from iPSCs, or by low concentration drug treatments. The proposed approach, easy to use and robust, opens the door to more accessible discovery of biological and disease biomarkers.

Link to article

L’article New publication in “Nature Communications” est apparu en premier sur Ksilink.

Chatrousse et al., Establishment of heterozygous and homozygous SHANK3 knockout clonal pluripotent stem cells from the parental hESC line SA001 using CRISPR/Cas9. https://doi.org/10.1016/j.scr.2023.103209

Phelan-McDermid syndrome (PMS) is a rare genetic disease characterized by a global developmental delay with autism spectrum disorder. PMS is caused by loss of function mutations in the SHANK3 gene leading to SHANK3 protein haploinsufficiency. This study describes the generation of isogenic clones produced from one male human embryonic stem cell line with deletions in SHANK3, in a heterozygous or homozygous manner, using CRISPR/Cas9 indel methodology. Differentiation of these clones into different neuronal lineages will help understanding PMS etiology and find treatments for PMS patients.

Article link: https://authors.elsevier.com/sd/article/S1873-5061(23)00195-2

L’article New publication in the journal ‘Stem Cell Research’ est apparu en premier sur Ksilink.

In this article and video, we describe a protocol to obtain and analyze phenotypic profiles from commercially available human mDA neurons using clustering or machine learning-driven supervised classification methods. The described protocol will particularly interest researchers working on neuronal disease modeling or studying chemical compound effects in human neurons.

This protocol was used in our study on Parkinson’s disease published in Stem Cell reports in September 2022 (read news here).

L’article New publication in Journal of Visualized Experiments est apparu en premier sur Ksilink.

The results of the groundbreaking study of the environMENTAL consortium shed light on how urban living environments can impact mental health through neurobiological pathways.

The research (article available here) identifies three distinct environmental profiles that are associated with depression, anxiety and emotional instability. Additionally, the authors identify brain regions that mediate the effect of different environmental profiles on psychiatric symptoms.

These findings have important implications for urban planners, city policymakers, and mental health professionals, helping to prioritize and target future public health interventions. It’s exciting research that can help shape the way we think about how urban environments affect our mental health.

We at Ksilink are proud to be part of the environMENTAL consortium and to contribute to these efforts to improve the lives of patients and citizens.

L’article A new publication of the environMENTAL consortium in Nature Medicine est apparu en premier sur Ksilink.

In our most recent work in collaboration with colleagues from Life & Brain and the University of Bonn we show how the combination of many image-derived quantitative features, and the use of machine learning (ML) can be useful for in vitro Parkinson’s disease modelling using human neurons. 

We started out with the idea that Parkinson’s Disease-specific signatures might be detectable based on the vast information digital images offer to us. Often researchers focus on a single phenotype only. We wanted to take many into account. We used two Parkinson’s Disease patient-derived iPSC models and the corresponding isogenic controls with repaired mutation. The first model carried a LRRK2 G2019S mutation, while in the second model the SNCA gene was triplicated. Both mutations have strong links with Parkinson’s disease. All cell lines were differentiated into midbrain dopaminergic neurons, the neuron type most vulnerable in Parkinson’s disease. The neurons were then stained, imaged and data was extracted from images. All steps were automated and are suitable for up-scaling. 126 morphological features covering intensities, correlations, shapes, textures, and other image-quantifiable elements were extracted. An additional 54 mitochondrial features were extracted using a separate staining panel. Next, we used supervised binary machine learning classifiers to group known and unknown classes into categories. The idea was that image features would be sufficient to classify neurons based on genotype or chemical compound treatment. 

First, we focused on genotype characterization. Our multi-feature approach classified neurons correctly using two different ML algorithms. “Unseen” healthy neurons classified similarly as isogenic controls. From a phenotypic perspective, “healthy” neurons therefore seem to be similar to each other. Secondly, we focused on chemical compounds and asked the question: Can chemical modulators shift a “Parkinson’s Disease” phenotypic profile towards a phenotypic profile more similar to “healthy cells”? We evaluated multiple molecules: Three LRRK2 kinase inhibitors and PEP005 and Prostratin, two molecules known to lower α-synuclein levels in cell culture. Two of the three LRRK2 kinase inhibitors shifted the “Parkinson’s Disease” phenotypic profile towards a “healthy” phenotypic profile. The rescue was partial because not all features were affected alike, highlighting the value of our approach to identifying features of interest. The same effect was observed when we included mitochondria-derived phenotypic features. Additionally, we tested PEP005 and Prostratin, both α-synuclein lowering compounds, and checked their effect on the phenotypic profiles of control and LRRK2 mutant neurons. Both molecules led to small, but detectable shifts toward a “healthy” profile. PEP005 and Prostratin effects became more pronounced when we slightly changed the staining panel and included the lysosomal marker LAMP2. Both molecules are assumed to act via the lysosome. To check whether chemical compound modulation of phenotypic profiles would also work in a completely different mutation background we repeated our experiments in dopaminergic neurons derived from an isogenic SNCA triplication model and obtained similar results. Finally, we showed in both models that phenotypic profiles are comparable after 37 and 50 days of differentiation, highlighting the stability of phenotypic profiles over time. 

In summary, we show that combining many subtle, but biological meaningful phenotypes, improves the predictive value of otherwise variable dopaminergic neuron models, for example for chemical compound characterization and eventually implementation in the drug discovery process. 

L’article New publication: Machine learning-aided analysis of human dopaminergic neurons est apparu en premier sur Ksilink.

As part of an international team, Ksilink contributed to the development of a patient-derived glioblastoma organoid (PD-GBO) platform for preclinical identification of new drugs against defined morphological glioblastoma features. In a proof-of concept study applied it to four patient tumors, the team demonstrated the utility of the platform as an empirical methodology to preclinically discover potentially effective treatments, producing actionable results within a period of about two weeks.

More details can be found in our recent publication (Ratliff, M.; Kim, H.; Qi, H.; Kim, M.; Ku, B.; Azorin, D.D.; Hausmann, D.; Khajuria, R.K.; Patel, A.; Maier, E.; Cousin, L.; Ogier, A.; Sahm, F.; Etminan, N.; Bunse, L.; Winkler, F.; El-Khoury, V.; Platten, M.; Kwon, Y.-J. Patient-Derived Tumor Organoids for Guidance of Personalized Drug Therapies in Recurrent Glioblastoma. Int. J. Mol. Sci. 2022, 23, 6572. https://doi.org/10.3390/ijms23126572).

L’article New publication: A step forward towards precision oncology est apparu en premier sur Ksilink.

L’article New publication in nature genetics est apparu en premier sur Ksilink.