Rigor and reproducibility in human brain organoid research: Where we are and where we need to go.

Human brain organoid models have emerged as a promising tool for studying human brain development and function. These models preserve human genetics and recapitulate some aspects of human brain development, while facilitating manipulation in an in vitro setting. Despite their potential to transform biology and medicine, concerns persist about their fidelity. To fully harness their potential, it is imperative to establish reliable analytic methods, ensuring rigor and reproducibility. Here, we review current analytical platforms used to characterize human forebrain cortical organoids, highlight challenges, and propose recommendations for future studies to achieve greater precision and uniformity across laboratories.

MALDI imaging mass spectrometry visualizes the distribution of antidepressant duloxetine and its major metabolites in mouse brain, liver, kidney, and spleen tissues.

Imaging mass spectrometry (IMS) is a powerful tool for mapping the spatial distribution of unlabeled drugs and metabolites that may find application in assessing drug delivery, explaining drug efficacy, and identifying potential toxicity. This study focuses on determining the spatial distribution of the antidepressant duloxetine, which is widely prescribed despite common adverse effects (liver injury, constant headaches) whose mechanisms are not fully understood. We utilized high-resolution IMS with matrix-assisted laser desorption/ionization (MALDI-IMS) to examine the distribution of duloxetine and its major metabolites in four mouse organs where it may contribute to efficacy or toxicity: brain, liver, kidney, and spleen. In none of these tissues is DLX or its metabolites homogeneously distributed, which has implications for both efficacy and toxicity. We found duloxetine to be similarly distributed in spleen red pulp and white pulp but differentially distributed in different anatomic regions of the liver, kidney, and brain, with dose-dependent patterns. Comparison with hematoxylin and eosin staining of tissue sections reveals that the ion images of endogenous lipids help delineate anatomic regions in the brain and kidney, while heme ion images assist in differentiating regions within the spleen. These endogenous metabolites may serve as a valuable resource for examining the spatial distribution of other drugs in tissues when staining images are not available. These findings may facilitate future mechanistic studies of the therapeutic and adverse effects of duloxetine. In the current work, we did not perform absolute quantification of duloxetine, which will be reported in due course Significance Statement The study utilized imaging mass spectrometry to examine the spatial distribution of duloxetine and its primary metabolites in mouse brain, liver, kidney and spleen. These results may pave the way for future investigations into the mechanisms behind duloxetine's therapeutic and adverse effects. Furthermore, the mass spectrometry images of specific endogenous metabolites such as heme could be valuable in analyzing the spatial distribution of other drugs within tissues in scenarios where histological staining images are unavailable.

Metabolic bioactivation of antidepressants: advance and underlying hepatotoxicity.

Many drugs that serve as first-line medications for the treatment of depression are associated with severe side effects, including liver injury. Of the 34 antidepressants discussed in this review, four have been withdrawn from the market due to severe hepatotoxicity, and others carry boxed warnings for idiosyncratic liver toxicity. The clinical and economic implications of antidepressant-induced liver injury are substantial, but the underlying mechanisms remain elusive. Drug-induced liver injury may involve the host immune system, the parent drug, or its metabolites, and reactive drug metabolites are one of the most commonly referenced risk factors. Although the precise mechanism by which toxicity is induced may be difficult to determine, identifying reactive metabolites that cause toxicity can offer valuable insights for decreasing the bioactivation potential of candidates during the drug discovery process. A comprehensive understanding of drug metabolic pathways can mitigate adverse drug-drug interactions that may be caused by elevated formation of reactive metabolites. This review provides a comprehensive overview of the current state of knowledge on antidepressant bioactivation, the metabolizing enzymes responsible for the formation of reactive metabolites, and their potential implication in hepatotoxicity. This information can be a valuable resource for medicinal chemists, toxicologists, and clinicians engaged in the fields of antidepressant development, toxicity, and depression treatment.

PolyAMiner-Bulk is a deep learning-based algorithm that decodes alternative polyadenylation dynamics from bulk RNA-seq data.

Alternative polyadenylation (APA) is a key post-transcriptional regulatory mechanism; yet, its regulation and impact on human diseases remain understudied. Existing bulk RNA sequencing (RNA-seq)-based APA methods predominantly rely on predefined annotations, severely impacting their ability to decode novel tissue- and disease-specific APA changes. Furthermore, they only account for the most proximal and distal cleavage and polyadenylation sites (C/PASs). Deconvoluting overlapping C/PASs and the inherent noisy 3' UTR coverage in bulk RNA-seq data pose additional challenges. To overcome these limitations, we introduce PolyAMiner-Bulk, an attention-based deep learning algorithm that accurately recapitulates C/PAS sequence grammar, resolves overlapping C/PASs, captures non-proximal-to-distal APA changes, and generates visualizations to illustrate APA dynamics. Evaluation on multiple datasets strongly evinces the performance merit of PolyAMiner-Bulk, accurately identifying more APA changes compared with other methods. With the growing importance of APA and the abundance of bulk RNA-seq data, PolyAMiner-Bulk establishes a robust paradigm of APA analysis.

MECP2-related disorders while gene-based therapies are on the horizon.

The emergence of new genetic tools has led to the discovery of the genetic bases of many intellectual and developmental disabilities. This creates exciting opportunities for research and treatment development, and a few genetic disorders (e.g., spinal muscular atrophy) have recently been treated with gene-based therapies. MECP2 is found on the X chromosome and regulates the transcription of thousands of genes. Loss of MECP2 gene product leads to Rett Syndrome, a disease found primarily in females, and is characterized by developmental regression, motor dysfunction, midline hand stereotypies, autonomic nervous system dysfunction, epilepsy, scoliosis, and autistic-like behavior. Duplication of MECP2 causes MECP2 Duplication Syndrome (MDS). MDS is found mostly in males and presents with developmental delay, hypotonia, autistic features, refractory epilepsy, and recurrent respiratory infections. While these two disorders share several characteristics, their differences (e.g., affected sex, age of onset, genotype/phenotype correlations) are important to distinguish in the light of gene-based therapy because they require opposite solutions. This review explores the clinical features of both disorders and highlights these important clinical differences.

Generation of five induced pluripotent stem cell lines from patients with MECP2 Duplication Syndrome.

MECP2 Duplication Syndrome (MDS) is a rare, severe neurodevelopmental disorder arising from duplications in the Xq28 region containing the MECP2 gene that predominantly affects males. We generated five human induced pluripotent stem cell (iPSC) lines from the fibroblasts of individuals carrying between 0.355 and 11.2 Mb size duplications in the chromosomal locus containing MECP2. All lines underwent extensive testing to confirm MECP2 duplication and iPSC-related features such as morphology, pluripotency markers, and trilineage differentiation potential. These lines are a valuable resource for molecular and functional studies of MDS as well as screening for a variety of therapeutic approaches.

SPA-STOCSY: an automated tool for identifying annotated and non-annotated metabolites in high-throughput NMR spectra.

MOTIVATION: Nuclear magnetic resonance spectroscopy (NMR) is widely used to analyze metabolites in biological samples, but the analysis requires specific expertise, it is time-consuming, and can be inaccurate. Here, we present a powerful automate tool, SPatial clustering Algorithm-Statistical TOtal Correlation SpectroscopY (SPA-STOCSY), which overcomes challenges faced when analyzing NMR data and identifies metabolites in a sample with high accuracy. RESULTS: As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset. It first investigates the covariance pattern among datapoints and then calculates the optimal threshold with which to cluster datapoints belonging to the same structural unit, i.e. the metabolite. Generated clusters are then automatically linked to a metabolite library to identify candidates. To assess SPA-STOCSY's efficiency and accuracy, we applied it to synthesized spectra and spectra acquired on Drosophila melanogaster tissue and human embryonic stem cells. In the synthesized spectra, SPA outperformed Statistical Recoupling of Variables (SRV), an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the biological data, SPA-STOCSY performed comparably to the operator-based Chenomx analysis while avoiding operator bias, and it required <7 min of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. It may thus accelerate the use of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making. AVAILABILITY AND IMPLEMENTATION: The codes of SPA-STOCSY are available at https://github.com/LiuzLab/SPA-STOCSY.

Mass spectrometry imaging as an emerging tool for studying metabolism in human brain organoids.

Human brain organoids are emerging models to study human brain development and pathology as they recapitulate the development and characteristics of major neural cell types, and enable manipulation through an in vitro system. Over the past decade, with the advent of spatial technologies, mass spectrometry imaging (MSI) has become a prominent tool for metabolic microscopy, providing label-free, non-targeted molecular and spatial distribution information of the metabolites within tissue, including lipids. This technology has never been used for studies of brain organoids and here, we set out to develop a standardized protocol for preparation and mass spectrometry imaging of human brain organoids. We present an optimized and validated sample preparation protocol, including sample fixation, optimal embedding solution, homogenous deposition of matrices, data acquisition and processing to maximize the molecular information derived from mass spectrometry imaging. We focus on lipids in organoids, as they play critical roles during cellular and brain development. Using high spatial and mass resolution in positive- and negative-ion modes, we detected 260 lipids in the organoids. Seven of them were uniquely localized within the neurogenic niches or rosettes as confirmed by histology, suggesting their importance for neuroprogenitor proliferation. We observed a particularly striking distribution of ceramide-phosphoethanolamine CerPE 36:1; O2 which was restricted within rosettes and of phosphatidyl-ethanolamine PE 38:3, which was distributed throughout the organoid tissue but not in rosettes. This suggests that ceramide in this particular lipid species might be important for neuroprogenitor biology, while its removal may be important for terminal differentiation of their progeny. Overall, our study establishes the first optimized experimental pipeline and data processing strategy for mass spectrometry imaging of human brain organoids, allowing direct comparison of lipid signal intensities and distributions in these tissues. Further, our data shed new light on the complex processes that govern brain development by identifying specific lipid signatures that may play a role in cell fate trajectories. Mass spectrometry imaging thus has great potential in advancing our understanding of early brain development as well as disease modeling and drug discovery.

SPA-STOCSY: An Automated Tool for Identification of Annotated and Non-Annotated Metabolites in High-Throughput NMR Spectra.

Nuclear Magnetic Resonance (NMR) spectroscopy is widely used to analyze metabolites in biological samples, but the analysis can be cumbersome and inaccurate. Here, we present a powerful automated tool, SPA-STOCSY (Spatial Clustering Algorithm - Statistical Total Correlation Spectroscopy), which overcomes the challenges by identifying metabolites in each sample with high accuracy. As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset, first investigating the covariance pattern and then calculating the optimal threshold with which to cluster data points belonging to the same structural unit, i.e. metabolite. The generated clusters are then automatically linked to a compound library to identify candidates. To assess SPA-STOCSY’s efficiency and accuracy, we applied it to synthesized and real NMR data obtained from Drosophila melanogaster brains and human embryonic stem cells. In the synthesized spectra, SPA outperforms Statistical Recoupling of Variables, an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the real spectra, SPA-STOCSY performs comparably to operator-based Chenomx analysis but avoids operator bias and performs the analyses in less than seven minutes of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. As such, it might accelerate the utilization of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making.

NMRQNet: a deep learning approach for automatic identification and quantification of metabolites using Nuclear Magnetic Resonance (NMR) in human plasma samples.

Nuclear Magnetic Resonance is a powerful platform that reveals the metabolomics profiles within biofluids or tissues and contributes to personalized treatments in medical practice. However, data volume and complexity hinder the exploration of NMR spectra. Besides, the lack of fast and accurate computational tools that can handle the automatic identification and quantification of essential metabolites from NMR spectra also slows the wide application of these techniques in clinical. We present NMRQNet, a deep-learning-based pipeline for automatic identification and quantification of dominant metabolite candidates within human plasma samples. The estimated relative concentrations could be further applied in statistical analysis to extract the potential biomarkers. We evaluate our method on multiple plasma samples, including species from mice to humans, curated using three anticoagulants, covering healthy and patient conditions in neurological disorder disease, greatly expanding the metabolomics analytical space in plasma. NMRQNet accurately reconstructed the original spectra and obtained significantly better quantification results than the earlier computational methods. Besides, NMRQNet also proposed relevant metabolites biomarkers that could potentially explain the risk factors associated with the condition. NMRQNet, with improved prediction performance, highlights the limitations in the existing approaches and has shown strong application potential for future metabolomics disease studies using plasma samples.

PolyAMiner-Bulk: A Machine Learning Based Bioinformatics Algorithm to Infer and Decode Alternative Polyadenylation Dynamics from bulk RNA-seq data.

More than half of human genes exercise alternative polyadenylation (APA) and generate mRNA transcripts with varying 3' untranslated regions (UTR). However, current computational approaches for identifying cleavage and polyadenylation sites (C/PASs) and quantifying 3'UTR length changes from bulk RNA-seq data fail to unravel tissue- and disease-specific APA dynamics. Here, we developed a next-generation bioinformatics algorithm and application, PolyAMiner-Bulk, that utilizes an attention-based machine learning architecture and an improved vector projection-based engine to infer differential APA dynamics accurately. When applied to earlier studies, PolyAMiner-Bulk accurately identified more than twice the number of APA changes in an RBM17 knockdown bulk RNA-seq dataset compared to current generation tools. Moreover, on a separate dataset, PolyAMiner-Bulk revealed novel APA dynamics and pathways in scleroderma pathology and identified differential APA in a gene that was identified as being involved in scleroderma pathogenesis in an independent study. Lastly, we used PolyAMiner-Bulk to analyze the RNA-seq data of post-mortem prefrontal cortexes from the ROSMAP data consortium and unraveled novel APA dynamics in Alzheimer's Disease. Our method, PolyAMiner-Bulk, creates a paradigm for future alternative polyadenylation analysis from bulk RNA-seq data.

Mutations in the transcriptional regulator MeCP2 severely impact key cellular and molecular signatures of human astrocytes during maturation.

Mutations in the MECP2 gene underlie a spectrum of neurodevelopmental disorders, most commonly Rett syndrome (RTT). We ask whether MECP2 mutations interfere with human astrocyte developmental maturation, thereby affecting their ability to support neurons. Using human-based models, we show that RTT-causing MECP2 mutations greatly impact the key role of astrocytes in regulating overall brain bioenergetics and that these metabolic aberrations are likely mediated by dysfunctional mitochondria. During post-natal maturation, astrocytes rely on neurons to induce their complex stellate morphology and transcriptional changes. While MECP2 mutations cause cell-intrinsic aberrations in the astrocyte transcriptional landscape, surprisingly, they do not affect the neuron-induced astrocyte gene expression. Notably, however, astrocytes are unable to develop complex mature morphology due to cell- and non-cell-autonomous aberrations caused by MECP2 mutations. Thus, MECP2 mutations critically impact key cellular and molecular features of human astrocytes and, hence, their ability to interact and support the structural and functional maturation of neurons.

Region-specific denoising identifies spatial co-expression patterns and intra-tissue heterogeneity in spatially resolved transcriptomics data.

Spatially resolved transcriptomics is a relatively new technique that maps transcriptional information within a tissue. Analysis of these datasets is challenging because gene expression values are highly sparse due to dropout events, and there is a lack of tools to facilitate in silico detection and annotation of regions based on their molecular content. Therefore, we develop a computational tool for detecting molecular regions and region-based Missing value Imputation for Spatially Transcriptomics (MIST). We validate MIST-identified regions across multiple datasets produced by 10x Visium Spatial Transcriptomics, using manually annotated histological images as references. We benchmark MIST against a spatial k-nearest neighboring baseline and other imputation methods designed for single-cell RNA sequencing. We use holdout experiments to demonstrate that MIST accurately recovers spatial transcriptomics missing values. MIST facilitates identifying intra-tissue heterogeneity and recovering spatial gene-gene co-expression signals. Using MIST before downstream analysis thus provides unbiased region detections to facilitate annotations with the associated functional analyses and produces accurately denoised spatial gene expression profiles.

Looking on the horizon; potential and unique approaches to developing radiation countermeasures for deep space travel.

Future lunar missions and beyond will require new and innovative approaches to radiation countermeasures. The Translational Research Institute for Space Health (TRISH) is focused on identifying and supporting unique approaches to reduce risks to human health and performance on future missions beyond low Earth orbit. This paper will describe three funded and complementary avenues for reducing the risk to humans from radiation exposure experienced in deep space. The first focus is on identifying new therapeutic targets to reduce the damaging effects of radiation by focusing on high throughput genetic screens in accessible, sometimes called lower, organism models. The second focus is to design innovative approaches for countermeasure development with special attention to nucleotide-based methodologies that may constitute a more agile way to design therapeutics. The final focus is to develop new and innovative ways to test radiation countermeasures in a human model system. While animal studies continue to be beneficial in the study of space radiation, they can have imperfect translation to humans. The use of three-dimensional (3D) complex in vitro models is a promising approach to aid the development of new countermeasures and personalized assessments of radiation risks. These three distinct and unique approaches complement traditional space radiation efforts and should provide future space explorers with more options to safeguard their short and long-term health.

Targeting hippocampal neurogenesis to protect astronauts' cognition and mood from decline due to space radiation effects.

Neurogenesis is an essential, lifelong process during which neural stem cells generate new neurons within the hippocampus, a center for learning, memory, and mood control. Neural stem cells are vulnerable to environmental insults spanning from chronic stress to radiation. These insults reduce their numbers and diminish neurogenesis, leading to memory decline, anxiety, and depression. Preserving neural stem cells could thus help prevent these neurogenesis-associated pathologies, an outcome particularly important for long-term space missions where environmental exposure to radiation is significantly higher than on Earth. Multiple developments, from mechanistic discoveries of radiation injury on hippocampal neurogenesis to new platforms for the development of selective, specific, effective, and safe small molecules as neurogenesis-protective agents hold great promise to minimize radiation damage on neurogenesis. In this review, we summarize the effects of space-like radiation on hippocampal neurogenesis. We then focus on current advances in drug discovery and development and discuss the nuclear receptor TLX/NR2E1 (oleic acid receptor) as an example of a neurogenic target that might rescue neurogenesis following radiation.

Spatial analysis of drug absorption, distribution, metabolism, and toxicology using mass spectrometry imaging.

Mass spectrometry imaging (MSI) is emerging as a powerful analytical tool for detection, quantification, and simultaneous spatial molecular imaging of endogenous and exogenous molecules via in situ mass spectrometry analysis of thin tissue sections without the requirement of chemical labeling. The MSI generates chemically specific and spatially resolved ion distribution information for administered drugs and metabolites, which allows numerous applications for studies involving various stages of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). MSI-based pharmacokinetic imaging analysis provides a histological context and cellular environment regarding dynamic drug distribution and metabolism processes, and facilitates the understanding of the spatial pharmacokinetics and pharmacodynamic properties of drugs. Herein, we discuss the MSI's current technological developments that offer qualitative, quantitative, and spatial location information of small molecule drugs, antibody, and oligonucleotides macromolecule drugs, and their metabolites in preclinical and clinical tissue specimens. We highlight the macro and micro drug-distribution in the whole-body, brain, lung, liver, kidney, stomach, intestine tissue sections, organoids, and the latest applications of MSI in pharmaceutical ADMET studies.

Oleic acid is an endogenous ligand of TLX/NR2E1 that triggers hippocampal neurogenesis.

Neural stem cells, the source of newborn neurons in the adult hippocampus, are intimately involved in learning and memory, mood, and stress response. Despite considerable progress in understanding the biology of neural stem cells and neurogenesis, regulating the neural stem cell population precisely has remained elusive because we have lacked the specific targets to stimulate their proliferation and neurogenesis. The orphan nuclear receptor TLX/NR2E1 governs neural stem and progenitor cell self-renewal and proliferation, but the precise mechanism by which it accomplishes this is not well understood because its endogenous ligand is not known. Here, we identify oleic acid (18:1ω9 monounsaturated fatty acid) as such a ligand. We first show that oleic acid is critical for neural stem cell survival. Next, we demonstrate that it binds to TLX to convert it from a transcriptional repressor to a transcriptional activator of cell-cycle and neurogenesis genes, which in turn increases neural stem cell mitotic activity and drives hippocampal neurogenesis in mice. Interestingly, oleic acid-activated TLX strongly up-regulates cell cycle genes while only modestly up-regulating neurogenic genes. We propose a model in which sufficient quantities of this endogenous ligand must bind to TLX to trigger the switch to proliferation and drive the progeny toward neuronal lineage. Oleic acid thus serves as a metabolic regulator of TLX activity that can be used to selectively target neural stem cells, paving the way for future therapeutic manipulations to counteract pathogenic impairments of neurogenesis.

Glial PAMPering and DAMPening of Adult Hippocampal Neurogenesis.

Adult neurogenesis represents a mature brain's capacity to integrate newly generated neurons into functional circuits. Impairment of neurogenesis contributes to the pathophysiology of various mood and cognitive disorders such as depression and Alzheimer's Disease. The hippocampal neurogenic niche hosts neural progenitors, glia, and vasculature, which all respond to intrinsic and environmental cues, helping determine their current state and ultimate fate. In this article we focus on the major immune communication pathways and mechanisms through which glial cells sense, interact with, and modulate the neurogenic niche. We pay particular attention to those related to the sensing of and response to innate immune danger signals. Receptors for danger signals were first discovered as a critical component of the innate immune system response to pathogens but are now also recognized to play a crucial role in modulating non-pathogenic sterile inflammation. In the neurogenic niche, viable, stressed, apoptotic, and dying cells can activate danger responses in neuroimmune cells, resulting in neuroprotection or neurotoxicity. Through these mechanisms glial cells can influence hippocampal stem cell fate, survival, neuronal maturation, and integration. Depending on the context, such responses may be appropriate and on-target, as in the case of learning-associated synaptic pruning, or excessive and off-target, as in neurodegenerative disorders.