Sp2 regulates late neurogenic but not early expansive divisions of neural stem cells underlying population growth in the mouse cortex

Dynamic Regulation OF The Chromatin Environment By Ash1L Modulates Human Neuronal Structure And Function

Precise regulation of the chromatin environment through post-translational histone modification modulates transcription and controls brain development. Not surprisingly, mutations in a large number of histone-modifying enzymes underlie complex brain disorders. In particular, the histone methyltransferase ASH1L modifies histone marks linked to transcriptional activation and has been implicated in multiple neuropsychiatric disorders. However, the mechanisms underlying the pathobiology of ASH1L-asociated disease remain underexplored. We generated human isogenic stem cells with a mutation in ASH1L's catalytic domain. We find that ASH1L dysfunction results in reduced neurite outgrowth, which correlates with alterations in the chromatin profile of activating and repressive histone marks, as well as the dysregulation of gene programs important for neuronal structure and function implicated in neuropsychiatric disease. We also identified a novel regulatory node implicating both the SP and Krüppel -like families of transcription factors and ASH1L relevant to human neuronal development. Finally, we rescue cellular defects linked to ASH1L dysfunction by leveraging two independent epigenetic mechanisms that promote transcriptional activation. In summary, we identified an ASH1L-driven epigenetic and transcriptional axis essential for human brain development and complex brain disorders that provide insights into future therapeutic strategies for ASH1L-related disorders.

Non-Invasive Quality Control of Organoid Cultures Using Mesofluidic CSTR Bioreactors and High-Content Imaging

Human brain organoids produce anatomically relevant cellular structures and recapitulate key aspects of in vivo brain function, which holds great potential to model neurological diseases and screen therapeutics. However, the long growth time of 3D systems complicates the culturing of brain organoids and results in heterogeneity across samples hampering their applications. We developed an integrated platform to enable robust and long-term culturing of 3D brain organoids. We designed a mesofluidic bioreactor device based on a reaction-diffusion scaling theory, which achieves robust media exchange for sufficient nutrient delivery in long-term culture. We integrated this device with longitudinal tracking and machine learning-based classification tools to enable non-invasive quality control of live organoids. This integrated platform allows for sample pre-selection for downstream molecular analysis. Transcriptome analyses of organoids revealed that our mesofluidic bioreactor promoted organoid development while reducing cell death. Our platform thus offers a generalizable tool to establish reproducible culture standards for 3D cellular systems for a variety of applications beyond brain organoids.

Phenotypic analysis with trans-recombination-based genetic mosaic models

Mosaicism refers to the presence of genetically distinct cell populations in an individual derived from a single zygote, which occurs during the process of development, aging, and genetic diseases. To date, a variety of genetically engineered mosaic analysis models have been established and widely used in studying gene function at exceptional cellular and spatiotemporal resolution, leading to many ground-breaking discoveries. Mosaic analysis with a repressible cellular marker and mosaic analysis with double markers are genetic mosaic analysis models based on trans-recombination. These models can generate sibling cells of distinct genotypes in the same animal and simultaneously label them with different colors. As a result, they offer a powerful approach for lineage tracing and studying the behavior of individual mutant cells in a wildtype environment, which is particularly useful for determining whether gene function is cell autonomous or nonautonomous. Here, we present a comprehensive review on the establishment and applications of mosaic analysis with a repressible cellular marker and mosaic analysis with double marker systems. Leveraging the capabilities of these mosaic models for phenotypic analysis will facilitate new discoveries on the cellular and molecular mechanisms of development and disease.

COMBINe enables automated detection and classification of neurons and astrocytes in tissue-cleared mouse brains

Tissue clearing renders entire organs transparent to accelerate whole-tissue imaging; for example, with light-sheet fluorescence microscopy. Yet, challenges remain in analyzing the large resulting 3D datasets that consist of terabytes of images and information on millions of labeled cells. Previous work has established pipelines for automated analysis of tissue-cleared mouse brains, but the focus there was on single-color channels and/or detection of nuclear localized signals in relatively low-resolution images. Here, we present an automated workflow (COMBINe, Cell detectiOn in Mouse BraIN) to map sparsely labeled neurons and astrocytes in genetically distinct mouse forebrains using mosaic analysis with double markers (MADM). COMBINe blends modules from multiple pipelines with RetinaNet at its core. We quantitatively analyzed the regional and subregional effects of MADM-based deletion of the epidermal growth factor receptor (EGFR) on neuronal and astrocyte populations in the mouse forebrain.

Deep learning for detecting and elucidating human T-cell leukemia virus type 1 integration in the human genome

Human T-cell leukemia virus type 1 (HTLV-1), a retrovirus, is the causative agent for adult T cell leukemia/lymphoma and many other human diseases. Accurate and high throughput detection of HTLV-1 virus integration sites (VISs) across the host genomes plays a crucial role in the prevention and treatment of HTLV-1-associated diseases. Here, we developed DeepHTLV, the first deep learning framework for VIS prediction de novo from genome sequence, motif discovery, and cis-regulatory factor identification. We demonstrated the high accuracy of DeepHTLV with more efficient and interpretive feature representations. Decoding the informative features captured by DeepHTLV resulted in eight representative clusters with consensus motifs for potential HTLV-1 integration. Furthermore, DeepHTLV revealed interesting cis-regulatory elements in regulation of VISs that have significant association with the detected motifs. Literature evidence demonstrated nearly half (34) of the predicted transcription factors enriched with VISs were involved in HTLV-1-associated diseases. DeepHTLV is freely available at https://github.com/bsml320/DeepHTLV.

Multicolor strategies for investigating clonal expansion and tissue plasticity

Understanding the generation of complexity in living organisms requires the use of lineage tracing tools at a multicellular scale. In this review, we describe the different multicolor strategies focusing on mouse models expressing several fluorescent reporter proteins, generated by classical (MADM, Brainbow and its multiple derivatives) or acute (StarTrack, CLoNe, MAGIC Markers, iOn, viral vectors) transgenesis. After detailing the multi-reporter genetic strategies that serve as a basis for the establishment of these multicolor mouse models, we briefly mention other animal and cellular models (zebrafish, chicken, drosophila, iPSC) that also rely on these constructs. Then, we highlight practical applications of multicolor mouse models to better understand organogenesis at single progenitor scale (clonal analyses) in the brain and briefly in several other tissues (intestine, skin, vascular, hematopoietic and immune systems). In addition, we detail the critical contribution of multicolor fate mapping strategies in apprehending the fine cellular choreography underlying tissue morphogenesis in several models with a particular focus on brain cytoarchitecture in health and diseases. Finally, we present the latest technological advances in multichannel and in-depth imaging, and automated analyses that enable to better exploit the large amount of data generated from multicolored tissues.

CRISPR single base editing, neuronal disease modelling and functional genomics for genetic variant analysis: pipeline validation using Kleefstra syndrome EHMT1 haploinsufficiency

Background

Over 400 million people worldwide are living with a rare disease. Next Generation Sequencing (NGS) identifies potential disease causative genetic variants. However, many are identified as variants of uncertain significance (VUS) and require functional laboratory validation to determine pathogenicity, and this creates major diagnostic delays.

Methods

In this study we test a rapid genetic variant assessment pipeline using CRISPR homology directed repair to introduce single nucleotide variants into inducible pluripotent stem cells (iPSCs), followed by neuronal disease modelling, and functional genomics on amplicon and RNA sequencing, to determine cellular changes to support patient diagnosis and identify disease mechanism.

Results

As proof-of-principle, we investigated an EHMT1 (Euchromatin histone methyltransferase 1; EHMT1 c.3430C > T; p.Gln1144*) genetic variant pathogenic for Kleefstra syndrome and determined changes in gene expression during neuronal progenitor cell differentiation. This pipeline rapidly identified Kleefstra syndrome in genetic variant cells compared to healthy cells, and revealed novel findings potentially implicating the key transcription factors REST and SP1 in disease pathogenesis.

Conclusion

The study pipeline is a rapid, robust method for genetic variant assessment that will support rare diseases patient diagnosis. The results also provide valuable information on genome wide perturbations key to disease mechanism that can be targeted for drug treatments.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13287-022-02740-3.

Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet

The ability to automatically detect and classify populations of cells in tissue sections is paramount in a wide variety of applications ranging from developmental biology to pathology. Although deep learning algorithms are widely applied to microscopy data, they typically focus on segmentation which requires extensive training and labor-intensive annotation. Here, we utilized object detection networks (neural networks) to detect and classify targets in complex microscopy images, while simplifying data annotation. To this end, we used a RetinaNet model to classify genetically labeled neurons and glia in the brains of Mosaic Analysis with Double Markers (MADM) mice. Our initial RetinaNet-based model achieved an average precision of 0.90 across six classes of cells differentiated by MADM reporter expression and their phenotype (neuron or glia). However, we found that a single RetinaNet model often failed when encountering dense and saturated glial clusters, which show high variability in their shape and fluorophore densities compared to neurons. To overcome this, we introduced a second RetinaNet model dedicated to the detection of glia clusters. Merging the predictions of the two computational models significantly improved the automated cell counting of glial clusters. The proposed cell detection workflow will be instrumental in quantitative analysis of the spatial organization of cellular populations, which is applicable not only to preparations in neuroscience studies, but also to any tissue preparation containing labeled populations of cells.

Deep learning-based autofocus method enhances image quality in light-sheet fluorescence microscopy

Light-sheet fluorescence microscopy (LSFM) is a minimally invasive and high throughput imaging technique ideal for capturing large volumes of tissue with sub-cellular resolution. A fundamental requirement for LSFM is a seamless overlap of the light-sheet that excites a selective plane in the specimen, with the focal plane of the objective lens. However, spatial heterogeneity in the refractive index of the specimen often results in violation of this requirement when imaging deep in the tissue. To address this issue, autofocus methods are commonly used to refocus the focal plane of the objective-lens on the light-sheet. Yet, autofocus techniques are slow since they require capturing a stack of images and tend to fail in the presence of spherical aberrations that dominate volume imaging. To address these issues, we present a deep learning-based autofocus framework that can estimate the position of the objective-lens focal plane relative to the light-sheet, based on two defocused images. This approach outperforms or provides comparable results with the best traditional autofocus method on small and large image patches respectively. When the trained network is integrated with a custom-built LSFM, a certainty measure is used to further refine the network's prediction. The network performance is demonstrated in real-time on cleared genetically labeled mouse forebrain and pig cochleae samples. Our study provides a framework that could improve light-sheet microscopy and its application toward imaging large 3D specimens with high spatial resolution.

The people behind the papers - Caroline Johnson and Troy Ghashghaei

Cortical development involves a switch from the self-amplification of stem cells to the generation of neuron and glia by progenitors. A new paper in Development investigates the molecular control of mitosis in these two stages, using simultaneous labelling and gene knockout in clones in the developing mouse brain. We caught up the paper's two authors Caroline Johnson and her supervisor Troy Ghashghaei, Professor of Neurobiology at the College of Veterinary Medicine at North Carolina State University, to find out more.