Human skeletal muscle organoids model fetal myogenesis and sustain uncommitted PAX7 myogenic progenitors

peer reviewedIn vitro culture systems that structurally model human myogenesis and promote PAX7+ myogenic progenitor maturation have not been established. Here we report that human skeletal muscle organoids can be differentiated from induced pluripotent stem cell lines to contain paraxial mesoderm and neuromesodermal progenitors and develop into organized structures reassembling neural plate border and dermomyotome. Culture conditions instigate neural lineage arrest and promote fetal hypaxial myogenesis toward limb axial anatomical identity, with generation of sustainable uncommitted PAX7 myogenic progenitors and fibroadipogenic (PDGFRa+) progenitor populations equivalent to those from the second trimester of human gestation. Single-cell comparison to human fetal and adult myogenic progenitor /satellite cells reveals distinct molecular signatures for non-dividing myogenic progenitors in activated (CD44High/CD98+/MYOD1+) and dormant (PAX7High/FBN1High/SPRY1High) states. Our approach provides a robust 3D in vitro developmental system for investigating muscle tissue morphogenesis and homeostasis

An archaeal compound as a driver of Parkinson’s disease pathogenesis

Patients with Parkinson’s disease (PD) exhibit differences in their gut microbiomes compared to healthy individuals. Although differences have most commonly been described in the abundances of bacterial taxa, changes to viral and archaeal populations have also been observed. Mechanistic links between gut microbes and PD pathogenesis remain elusive but could involve molecules that promote α-synuclein aggregation. Here, we show that 2-hydroxypyridine (2-HP) represents a key molecule for the pathogenesis of PD. We observe significantly elevated 2-HP levels in faecal samples from patients with PD or its prodrome, idiopathic REM sleep behaviour disorder (iRBD), compared to healthy controls. 2-HP is correlated with the archaeal species Methanobrevibacter smithii and with genes involved in methane metabolism, and it is detectable in isolate cultures of M. smithii. We demonstrate that 2-HP is selectively toxic to transgenic α-synuclein overexpressing yeast and increases α-synuclein aggregation in a yeast model as well as in human induced pluripotent stem cell derived enteric neurons. It also exacerbates PD-related motor symptoms, α-synuclein aggregation, and striatal degeneration when injected intrastriatally in transgenic mice overexpressing human α-synuclein. Our results highlight the effect of an archaeal molecule in relation to the gut-brain axis, which is critical for the diagnosis, prognosis, and treatment of PD.

Automated high-throughput high-content autophagy and mitophagy analysis platform

Autophagic processes play a central role in cellular homeostasis. In pathological conditions, the flow of autophagy can be affected at multiple and distinct steps of the pathway. Current analyses tools do not deliver the required detail for dissecting pathway intermediates. The development of new tools to analyze autophagic processes qualitatively and quantitatively in a more straightforward manner is required. Defining all autophagy pathway intermediates in a high-throughput manner is technologically challenging and has not been addressed yet. Here, we overcome those requirements and limitations by the developed of stable autophagy and mitophagy reporter-iPSC and the establishment of a novel high-throughput phenotyping platform utilizing automated high-content image analysis to assess autophagy and mitophagy pathway intermediates

FACS-Assisted CRISPR-Cas9 Genome Editing Facilitates Parkinson's Disease Modeling

Genome editing and human induced pluripotent stem cells hold great promise for the development of isogenic disease models and the correction of disease-associated mutations for isogenic tissue therapy. CRISPR-Cas9 has emerged as a versatile and simple tool for engineering human cells for such purposes. However, the current protocols to derive genome-edited lines require the screening of a great number of clones to obtain one free of random integration or on-locus non-homologous end joining (NHEJ)-containing alleles. Here, we describe an efficient method to derive biallelic genome-edited populations by the use of fluorescent markers. We call this technique FACS-assisted CRISPR-Cas9 editing (FACE). FACE allows the derivation of correctly edited polyclones carrying a positive selection fluorescent module and the exclusion of non-edited, random integrations and on-target allele NHEJ-containing cells. We derived a set of isogenic lines containing Parkinson's-disease-associated mutations in α-synuclein and present their comparative phenotypes

From tech to bench: Deep Learning pipeline for image segmentation of high-throughput high-content microscopy data

Automation of biological image analysis is essential to boost biomedical research. The study of complex diseases such as neurodegenerative diseases calls for big amounts of data to build models towards precision medicine. Such data acquisition is feasible in the context of high-throughput screening in which the quality of the results relays on the accuracy of image analysis. Although the state-of-the-art solutions for image segmentation employ deep learning approaches, the high cost of manual data curation is hampering the real use in current biomedical research laboratories. Here, we propose a pipeline that employs deep learning not only to conduct accurate segmentation but also to assist with the creation of high-quality datasets in a less time-consuming solution for the experts. Weakly-labelled datasets are becoming a common alternative as a starting point to develop real-world solutions. Traditional approaches based on classical multimedia signal processing were employed to generate a pipeline specifically optimized for the high-throughput screening images of iPSC fused with rosella biosensor. Such pipeline produced good segmentation results but with several inaccuracies. We employed the weakly-labelled masks produced in this pipeline to train a multiclass semantic segmentation CNN solution based on U-net architecture. Since a strong class imbalance was detected between the classes, we employed a class sensitive cost function: Dice coe!cient. Next, we evaluated the accuracy between the weakly-labelled data and the trained network segmentation using double-blind tests conducted by experts in cell biology with experience in this type of images; as well as traditional metrics to evaluate the quality of the segmentation using manually curated segmentations by cell biology experts. In all the evaluations the prediction of the neural network overcomes the weakly-labelled data quality segmentation. Another big handicap that complicates the use of deep learning solutions in wet lab environments is the lack of user-friendly tools for non-computational experts such as biologists. To complete our solution, we integrated the trained network on a GUI built on MATLAB environment with non-programming requirements for the user. This integration allows conducting semantic segmentation of microscopy images in a few seconds. In addition, thanks to the patch-based approach it can be employed in images with different sizes. Finally, the human-experts can correct the potential inaccuracies of the prediction in a simple interactive way which can be easily stored and employed to re-train the network to improve its accuracy. In conclusion, our solution focuses on two important bottlenecks to translate leading-edge technologies in computer vision to biomedical research: On one hand, the effortless obtention of high-quality datasets with expertise supervision taking advantage of the proven ability of our CNN solution to generalize from weakly-labelled inaccuracies. On the other hand, the ease of use provided by the GUI integration of our solution to both segment images and interact with the predicted output. Overall this approach looks promising for fast adaptability to new scenarios

Generalising from Conventional Pipelines: A Case Study in Deep Learning-Based High-Throughput Screening

The study of complex diseases relies on large amounts of data to build models toward precision medicine. Such data acquisition is feasible in the context of high-throughput screening, in which the quality of the results relies on the accuracy of the image analysis. Although state-of-the-art solutions for image segmentation employ deep learning approaches, the high cost of manually generating ground truth labels for model training hampers the day-to-day application in experimental laboratories. Alternatively, traditional computer vision-based solutions do not need expensive labels for their implementation. Our work combines both approaches by training a deep learning network using weak training labels automatically generated with conventional computer vision methods. Our network surpasses the conventional segmentation quality by generalising beyond noisy labels, providing a 25 % increase of mean intersection over union, and simultaneously reducing the development and inference times. Our solution was embedded into an easy-to-use graphical user interface that allows researchers to assess the predictions and correct potential inaccuracies with minimal human input. To demonstrate the feasibility of training a deep learning solution on a large dataset of noisy labels automatically generated by a conventional pipeline, we compared our solution against the common approach of training a model from a small manually curated dataset by several experts. Our work suggests that humans perform better in context interpretation, such as error assessment, while computers outperform in pixel-by-pixel fine segmentation. Such pipelines are illustrated with a case study on image segmentation for autophagy events. This work aims for better translation of new technologies to real-world settings in microscopy-image analysis.v-

Synapse alterations precede neuronal damage and storage pathology in a human cerebral organoid model of CLN3-juvenile neuronal ceroid lipofuscinosis

The juvenile form of neuronal ceroid Lipofuscinosis (JNCL) is the most common form within this group of rare lysosomal storage disorders, causing pediatric neurodegeneration. The genetic disorder, which is caused by recessive mutations affecting the CLN3 gene, features progressive vision loss, cognitive and motor decline and other psychiatric conditions, seizure episodes, leading to premature death. Animal models have traditionally aid the understanding of the disease mechanisms and pathology and are very relevant for biomarker research and therapeutic testing. Nevertheless, there is a need for establishing reliable and predictive human cellular models to study the disease. Since patient material, particularly from children, is scarce and difficult to obtain, we generated an engineered a CLN3-mutant isogenic human induced pluripotent stem cell (hiPSC) line carrying the c.1054C → T pathologic variant, using state of the art CRISPR/Cas9 technology. To prove the suitability of the isogenic pair to model JNCL, we screened for disease-specific phenotypes in non-neuronal two-dimensional cell culture models as well as in cerebral brain organoids. Our data demonstrates that the sole introduction of the pathogenic variant gives rise to classical hallmarks of JNCL in vitro. Additionally, we discovered an alteration of the splicing caused by this particular mutation. Next, we derived cerebral organoids and used them as a neurodevelopmental model to study the particular effects of the CLN3Q352X mutation during brain formation in the disease context. About half of the mutation -carrying cerebral organoids completely failed to develop normally. The other half, which escaped this severe defect were used for the analysis of more subtle alterations. In these escapers, whole-transcriptome analysis demonstrated early disease signatures, affecting pathways related to development, corticogenesis and synapses. Complementary metabolomics analysis confirmed decreased levels of cerebral tissue metabolites, some particularly relevant for synapse formation and neurotransmission, such as gamma-amino butyric acid (GABA). Our data suggests that a mutation in CLN3 severely affects brain development. Furthermore, before disease onset, disease -associated neurodevelopmental changes, particular concerning synapse formation and function, occur

Synapse alterations precede neuronal damage and storage pathology in a human cerebral organoid model of CLN3-juvenile neuronal ceroid lipofuscinosis

The juvenile form of neuronal ceroid Lipofuscinosis (JNCL) is the most common form within this group of rare lysosomal storage disorders, causing pediatric neurodegeneration. The genetic disorder, which is caused by recessive mutations affecting the CLN3 gene, features progressive vision loss, cognitive and motor decline and other psychiatric conditions, seizure episodes, leading to premature death. Animal models have traditionally aid the understanding of the disease mechanisms and pathology and are very relevant for biomarker research and therapeutic testing. Nevertheless, there is a need for establishing reliable and predictive human cellular models to study the disease. Since patient material, particularly from children, is scarce and difficult to obtain, we generated an engineered a CLN3-mutant isogenic human induced pluripotent stem cell (hiPSC) line carrying the c.1054C → T pathologic variant, using state of the art CRISPR/Cas9 technology. To prove the suitability of the isogenic pair to model JNCL, we screened for disease-specific phenotypes in non-neuronal two-dimensional cell culture models as well as in cerebral brain organoids. Our data demonstrates that the sole introduction of the pathogenic variant gives rise to classical hallmarks of JNCL in vitro. Additionally, we discovered an alteration of the splicing caused by this particular mutation. Next, we derived cerebral organoids and used them as a neurodevelopmental model to study the particular effects of the CLN3Q352X mutation during brain formation in the disease context. About half of the mutation -carrying cerebral organoids completely failed to develop normally. The other half, which escaped this severe defect were used for the analysis of more subtle alterations. In these escapers, whole-transcriptome analysis demonstrated early disease signatures, affecting pathways related to development, corticogenesis and synapses. Complementary metabolomics analysis confirmed decreased levels of cerebral tissue metabolites, some particularly relevant for synapse formation and neurotransmission, such as gamma-amino butyric acid (GABA). Our data suggests that a mutation in CLN3 severely affects brain development. Furthermore, before disease onset, disease -associated neurodevelopmental changes, particular concerning synapse formation and function, occur

PARK7/DJ-1 promotes pyruvate dehydrogenase activity and maintains T(reg) homeostasis during ageing.

Pyruvate dehydrogenase (PDH) is the gatekeeper enzyme of the tricarboxylic acid (TCA) cycle. Here we show that the deglycase DJ-1 (encoded by PARK7, a key familial Parkinson's disease gene) is a pacemaker regulating PDH activity in CD4(+) regulatory T cells (T(reg) cells). DJ-1 binds to PDHE1-β (PDHB), inhibiting phosphorylation of PDHE1-α (PDHA), thus promoting PDH activity and oxidative phosphorylation (OXPHOS). Park7 (Dj-1) deletion impairs T(reg) survival starting in young mice and reduces T(reg) homeostatic proliferation and cellularity only in aged mice. This leads to increased severity in aged mice during the remission of experimental autoimmune encephalomyelitis (EAE). Dj-1 deletion also compromises differentiation of inducible T(reg) cells especially in aged mice, and the impairment occurs via regulation of PDHB. These findings provide unforeseen insight into the complicated regulatory machinery of the PDH complex. As T(reg) homeostasis is dysregulated in many complex diseases, the DJ-1-PDHB axis represents a potential target to maintain or re-establish T(reg) homeostasis