Three dimensional (3D) and four dimensional (4D) live imaging to study mechanisms of progressive neurodegeneration.

Neurodegenerative diseases are complex and progressive, posing challenges to their study and understanding. Recent advances in microscopy imaging technologies have enabled the exploration of neurons in three spatial dimensions (3D) over time (4D). When applied to 3D cultures, tissues or animals, these technologies can provide valuable insights into the dynamic and spatial nature of neurodegenerative diseases. This review focuses on the use of imaging techniques and neurodegenerative disease models to study neurodegeneration in 4D. Imaging techniques such as confocal microscopy, two-photon microscopy, miniscope imaging, light sheet microscopy, and robotic microscopy offer powerful tools to visualize and analyze neuronal changes over time in 3D tissue. Application of these technologies to in vitro models of neurodegeneration such as mouse organotypic culture systems and human organoid models provide versatile platforms to study neurodegeneration in a physiologically relevant context. Additionally, use of 4D imaging in vivo, including in mouse and zebrafish models of neurodegenerative diseases, allows for the investigation of early dysfunction and behavioral changes associated with neurodegeneration. We propose that these studies have the power to overcome the limitations of two-dimensional (2D) monolayer neuronal cultures, and pave the way for improved understanding of the dynamics of neurodegenerative diseases and the development of effective therapeutic strategies.

A dynamic in vitro model of Down syndrome neurogenesis with trisomy 21 gene dosage correction.

Excess gene dosage from chromosome 21 (chr21) causes Down syndrome (DS), spanning developmental and acute phenotypes in terminal cell types. Which phenotypes remain amenable to intervention after development is unknown. To address this question in a model of DS neurogenesis, we derived trisomy 21 (T21) human induced pluripotent stem cells (iPSCs) alongside, otherwise, isogenic euploid controls from mosaic DS fibroblasts and equipped one chr21 copy with an inducible XIST transgene. Monoallelic chr21 silencing by XIST is near-complete and irreversible in iPSCs. Differential expression reveals that T21 neural lineages and iPSCs share suppressed translation and mitochondrial pathways and activate cellular stress responses. When XIST is induced before the neural progenitor stage, T21 dosage correction suppresses a pronounced skew toward astrogenesis in neural differentiation. Because our transgene remains inducible in postmitotic T21 neurons and astrocytes, we demonstrate that XIST efficiently represses genes even after terminal differentiation, which will empower exploration of cell type-specific T21 phenotypes that remain responsive to chr21 dosage.

A Trisomy 21-linked Hematopoietic Gene Variant in Microglia Confers Resilience in Human iPSC Models of Alzheimer's Disease.

While challenging, identifying individuals displaying resilience to Alzheimer's disease (AD) and understanding the underlying mechanism holds great promise for the development of new therapeutic interventions to effectively treat AD. Down syndrome (DS), or trisomy 21, is the most common genetic cause of AD. Interestingly, some people with DS, despite developing AD neuropathology, show resilience to cognitive decline. Furthermore, DS individuals are at an increased risk of myeloid leukemia due to somatic mutations in hematopoietic cells. Recent studies indicate that somatic mutations in hematopoietic cells may lead to resilience to neurodegeneration. Microglia, derived from hematopoietic lineages, play a central role in AD etiology. We therefore hypothesize that microglia carrying the somatic mutations associated with DS myeloid leukemia may impart resilience to AD. Using CRISPR-Cas9 gene editing, we introduce a trisomy 21-linked hotspot CSF2RB A455D mutation into human pluripotent stem cell (hPSC) lines derived from both DS and healthy individuals. Employing hPSC-based in vitro microglia culture and in vivo human microglia chimeric mouse brain models, we show that in response to pathological tau, the CSF2RB A455D mutation suppresses microglial type-1 interferon signaling, independent of trisomy 21 genetic background. This mutation reduces neuroinflammation and enhances phagocytic and autophagic functions, thereby ameliorating senescent and dystrophic phenotypes in human microglia. Moreover, the CSF2RB A455D mutation promotes the development of a unique microglia subcluster with tissue repair properties. Importantly, human microglia carrying CSF2RB A455D provide protection to neuronal function, such as neurogenesis and synaptic plasticity in chimeric mouse brains where human microglia largely repopulate the hippocampus. When co-transplanted into the same mouse brains, human microglia with CSF2RB A455D mutation phagocytize and replace human microglia carrying the wildtype CSF2RB gene following pathological tau treatment. Our findings suggest that hPSC-derived CSF2RB A455D microglia could be employed to develop effective microglial replacement therapy for AD and other age-related neurodegenerative diseases, even without the need to deplete endogenous diseased microglia prior to cell transplantation.

A foundational atlas of autism protein interactions reveals molecular convergence.

Translating high-confidence (hc) autism spectrum disorder (ASD) genes into viable treatment targets remains elusive. We constructed a foundational protein-protein interaction (PPI) network in HEK293T cells involving 100 hcASD risk genes, revealing over 1,800 PPIs (87% novel). Interactors, expressed in the human brain and enriched for ASD but not schizophrenia genetic risk, converged on protein complexes involved in neurogenesis, tubulin biology, transcriptional regulation, and chromatin modification. A PPI map of 54 patient-derived missense variants identified differential physical interactions, and we leveraged AlphaFold-Multimer predictions to prioritize direct PPIs and specific variants for interrogation in Xenopus tropicalis and human forebrain organoids. A mutation in the transcription factor FOXP1 led to reconfiguration of DNA binding sites and altered development of deep cortical layer neurons in forebrain organoids. This work offers new insights into molecular mechanisms underlying ASD and describes a powerful platform to develop and test therapeutic strategies for many genetically-defined conditions.

Enhancing Spatial Transcriptomics Analysis by Integrating Image-Aware Deep Learning Methods.

Spatial transcriptomics (ST) represents a pivotal advancement in biomedical research, enabling the transcriptional profiling of cells within their morphological context and providing a pivotal tool for understanding spatial heterogeneity in cancer tissues. However, current analytical approaches, akin to single-cell analysis, largely depend on gene expression, underutilizing the rich morphological information inherent in the tissue. We present a novel method integrating spatial transcriptomics and histopathological image data to better capture biologically meaningful patterns in patient data, focusing on aggressive cancer types such as glioblastoma and triple-negative breast cancer. We used a ResNet-based deep learning model to extract key morphological features from high-resolution whole-slide histology images. Spot-level PCA-reduced vectors of both the ResNet-50 analysis of the histological image and the spatial gene expression data were used in Louvain clustering to enable image-aware feature discovery. Assessment of features from image-aware clustering successfully pinpointed key biological features identified by manual histopathology, such as for regions of fibrosis and necrosis, as well as improved edge definition in EGFR-rich areas. Importantly, our combinatorial approach revealed crucial characteristics seen in histopathology that gene-expression-only analysis had missed.Supplemental Material: https://github.com/davcraig75/song_psb2014/blob/main/SupplementaryData.pdf.

AutoComet: A fully automated algorithm to quickly and accurately analyze comet assays.

DNA damage is a common cellular feature seen in cancer and neurodegenerative disease, but fast and accurate methods for quantifying DNA damage are lacking. Comet assays are a biochemical tool to measure DNA damage based on the migration of broken DNA strands towards a positive electrode, which creates a quantifiable 'tail' behind the cell. However, a major limitation of this approach is the time needed for analysis of comets in the images with available open-source algorithms. The requirement for manual curation and the laborious pre- and post-processing steps can take hours to days. To overcome these limitations, we developed AutoComet, a new open-source algorithm for comet analysis that utilizes automated comet segmentation and quantification of tail parameters. AutoComet first segments and filters comets based on size and intensity and then filters out comets without a well-connected head and tail, which significantly increases segmentation accuracy. Because AutoComet is fully automated, it minimizes curator bias and is scalable, decreasing analysis time over ten-fold, to less than 3 s per comet. AutoComet successfully detected statistically significant differences in tail parameters between cells with and without induced DNA damage, and was more comparable to the results of manual curation than other open-source software analysis programs. We conclude that the AutoComet algorithm provides a fast, unbiased and accurate method to quantify DNA damage that avoids the inherent limitations of manual curation and will significantly improve the ability to detect DNA damage.

The Foundational Data Initiative for Parkinson Disease: Enabling efficient translation from genetic maps to mechanism.

The Foundational Data Initiative for Parkinson Disease (FOUNDIN-PD) is an international collaboration producing fundamental resources for Parkinson disease (PD). FOUNDIN-PD generated a multi-layered molecular dataset in a cohort of induced pluripotent stem cell (iPSC) lines differentiated to dopaminergic (DA) neurons, a major affected cell type in PD. The lines were derived from the Parkinson's Progression Markers Initiative study, which included participants with PD carrying monogenic PD variants, variants with intermediate effects, and variants identified by genome-wide association studies and unaffected individuals. We generated genetic, epigenetic, regulatory, transcriptomic, and longitudinal cellular imaging data from iPSC-derived DA neurons to understand molecular relationships between disease-associated genetic variation and proximate molecular events. These data reveal that iPSC-derived DA neurons provide a valuable cellular context and foundational atlas for modeling PD genetic risk. We have integrated these data into a FOUNDIN-PD data browser as a resource for understanding the molecular pathogenesis of PD.

Large-scale differentiation of iPSC-derived motor neurons from ALS and control subjects.

Using induced pluripotent stem cells (iPSCs) to understand the mechanisms of neurological disease holds great promise; however, there is a lack of well-curated lines from a large array of participants. Answer ALS has generated over 1,000 iPSC lines from control and amyotrophic lateral sclerosis (ALS) patients along with clinical and whole-genome sequencing data. The current report summarizes cell marker and gene expression in motor neuron cultures derived from 92 healthy control and 341 ALS participants using a 32-day differentiation protocol. This is the largest set of iPSCs to be differentiated into motor neurons, and characterization suggests that cell composition and sex are significant sources of variability that need to be carefully controlled for in future studies. These data are reported as a resource for the scientific community that will utilize Answer ALS data for disease modeling using a wider array of omics being made available for these samples.

Frontotemporal Dementia Patient Neurons With Progranulin Deficiency Display Protein Dyshomeostasis.

Haploinsufficiency of progranulin (PGRN) causes frontotemporal dementia (FTD), a devastating neurodegenerative disease with no effective treatment. PGRN is required for efficient proteostasis, as loss of neuronal PGRN results in dysfunctional lysosomes and impaired clearance and cytoplasmic aggregation of TDP-43, a protein involved in neurodegeneration in FTD. These and other events lead to neurodegeneration and neuroinflammation. However, the detailed mechanisms leading to protein dyshomeostasis in PGRN-deficient cells remain unclear. We report here the development of human cell models of FTD with PGRN-deficiency to explore the molecular mechanisms underlying proteostasis breakdown and TDP-43 aggregation in FTD. Neurons differentiated from FTD patient induced pluripotent stem cells (iPSCs) have reduced PGRN levels, and the neurons recapitulate key disease features, including impaired lysosomal function, defective TDP-43 turnover and accumulation, neurodegeneration, and death. Proteomic analysis revealed altered levels of proteins linked to the autophagy-lysosome pathway (ALP) and the ubiquitin-proteasome system (UPS) in FTD patient neurons, providing new mechanistic insights into the link between PGRN-deficiency and disease pathobiology.

Nuclear accumulation of host transcripts during Zika Virus Infection.

Zika virus (ZIKV) infects fetal neural progenitor cells (NPCs) causing severe neurodevelopmental disorders in utero. Multiple pathways involved in normal brain development are dysfunctional in infected NPCs but how ZIKV centrally reprograms these pathways remains unknown. Here we show that ZIKV infection disrupts subcellular partitioning of host transcripts critical for neurodevelopment in NPCs and functionally link this process to the up-frameshift protein 1 (UPF1). UPF1 is an RNA-binding protein known to regulate decay of cellular and viral RNAs and is less expressed in ZIKV-infected cells. Using infrared crosslinking immunoprecipitation and RNA sequencing (irCLIP-Seq), we show that a subset of mRNAs loses UPF1 binding in ZIKV-infected NPCs, consistent with UPF1's diminished expression. UPF1 target transcripts, however, are not altered in abundance but in subcellular localization, with mRNAs accumulating in the nucleus of infected or UPF1 knockdown cells. This leads to diminished protein expression of FREM2, a protein required for maintenance of NPC identity. Our results newly link UPF1 to the regulation of mRNA transport in NPCs, a process perturbed during ZIKV infection.

Fluorescently labeled nuclear morphology is highly informative of neurotoxicity.

Neurotoxicity can be detected in live microscopy by morphological changes such as retraction of neurites, fragmentation, blebbing of the neuronal soma and ultimately the disappearance of fluorescently labeled neurons. However, quantification of these features is often difficult, low-throughput, and imprecise due to the overreliance on human curation. Recently, we showed that convolutional neural network (CNN) models can outperform human curators in the assessment of neuronal death from images of fluorescently labeled neurons, suggesting that there is information within the images that indicates toxicity but that is not apparent to the human eye. In particular, the CNN's decision strategy indicated that information within the nuclear region was essential for its superhuman performance. Here, we systematically tested this prediction by comparing images of fluorescent neuronal morphology from nuclear-localized fluorescent protein to those from freely diffused fluorescent protein for classifying neuronal death. We found that biomarker-optimized (BO-) CNNs could learn to classify neuronal death from fluorescent protein-localized nuclear morphology (mApple-NLS-CNN) alone, with super-human accuracy. Furthermore, leveraging methods from explainable artificial intelligence, we identified novel features within the nuclear-localized fluorescent protein signal that were indicative of neuronal death. Our findings suggest that the use of a nuclear morphology marker in live imaging combined with computational models such mApple-NLS-CNN can provide an optimal readout of neuronal death, a common result of neurotoxicity.

Huntington's disease iPSC models-using human patient cells to understand the pathology caused by expanded CAG repeats.

A major advance in the study of Huntington's disease (HD) has been the development of human disease models employing induced pluripotent stem cells (iPSCs) derived from patients with HD. Because iPSCs provide an unlimited source of cells and can be obtained from large numbers of HD patients, they are a uniquely valuable tool for investigating disease mechanisms and for discovering potential disease-modifying therapeutics. Here, we summarize some of the important findings in HD pathophysiology that have emerged from studies of patient-derived iPSC lines. Because they retain the genome and actual disease mutations of the patient, they provide a cell source to investigate genetic contributions to the disease. iPSCs provide advantages over other disease models. While iPSC-based technology erases some epigenetic marks, newly developed transdifferentiation methods now let us investigate epigenetic factors that control expression of mutant huntingtin (mHTT). Human HD iPSC lines allow us to investigate how endogenous levels of mHTT affect cell health, in contrast to other models that often rely on overexpressing the protein. iPSCs can be differentiated into neurons and other disease-related cells such as astrocytes from different brain regions to study brain regional differences in the disease process, as well as the cell-cell dependencies involved in HD-associated neurodegeneration. They also serve as a tissue source to investigate factors that impact CAG repeat instability, which is involved in regional differences in neurodegeneration in the HD brain. Human iPSC models can serve as a powerful model system to identify genetic modifiers that may impact disease onset, progression, and symptomatology, providing novel molecular targets for drug discovery.

Generation of two human induced pluripotent stem cell lines from fibroblasts of Parkinson's disease patients carrying the ILE368ASN mutation in PINK1 (LCSBi002) and the R275W mutation in Parkin (LCSBI004).

Mutations in PINK1 and Parkin are two of the main causes of recessive early-onset Parkinson's disease (PD). We generated human induced pluripotent stem cells (hiPSCs) from fibroblasts of a 64-year-old male patient with a homozygous ILE368ASN mutation in PINK1, who experienced disease onset at 33 years, and from fibroblasts of a 61-year-old female patient heterozygous for the R275W mutation in Parkin, who experienced disease onset at 44 years. Array comparative genomic hybridization (aCGH) determined genotypic variation in each line. The cell lines were successfully used to generate midbrain dopaminergic neurons, the neuron type primarily affected in PD.

Single-cell transcriptomics of human iPSC differentiation dynamics reveal a core molecular network of Parkinson's disease.

Parkinson's disease (PD) is the second-most prevalent neurodegenerative disorder, characterized by the loss of dopaminergic neurons (mDA) in the midbrain. The underlying mechanisms are only partly understood and there is no treatment to reverse PD progression. Here, we investigated the disease mechanism using mDA neurons differentiated from human induced pluripotent stem cells (hiPSCs) carrying the ILE368ASN mutation within the PINK1 gene, which is strongly associated with PD. Single-cell RNA sequencing (RNAseq) and gene expression analysis of a PINK1-ILE368ASN and a control cell line identified genes differentially expressed during mDA neuron differentiation. Network analysis revealed that these genes form a core network, members of which interact with all known 19 protein-coding Parkinson's disease-associated genes. This core network encompasses key PD-associated pathways, including ubiquitination, mitochondrial function, protein processing, RNA metabolism, and vesicular transport. Proteomics analysis showed a consistent alteration in proteins of dopamine metabolism, indicating a defect of dopaminergic metabolism in PINK1-ILE368ASN neurons. Our findings suggest the existence of a network onto which pathways associated with PD pathology converge, and offers an inclusive interpretation of the phenotypic heterogeneity of PD.

Superhuman cell death detection with biomarker-optimized neural networks.

Cellular events underlying neurodegenerative disease may be captured by longitudinal live microscopy of neurons. While the advent of robot-assisted microscopy has helped scale such efforts to high-throughput regimes with the statistical power to detect transient events, time-intensive human annotation is required. We addressed this fundamental limitation with biomarker-optimized convolutional neural networks (BO-CNNs): interpretable computer vision models trained directly on biosensor activity. We demonstrate the ability of BO-CNNs to detect cell death, which is typically measured by trained annotators. BO-CNNs detected cell death with superhuman accuracy and speed by learning to identify subcellular morphology associated with cell vitality, despite receiving no explicit supervision to rely on these features. These models also revealed an intranuclear morphology signal that is difficult to spot by eye and had not previously been linked to cell death, but that reliably indicates death. BO-CNNs are broadly useful for analyzing live microscopy and essential for interpreting high-throughput experiments.

An integrated multi-omic analysis of iPSC-derived motor neurons from C9ORF72 ALS patients.

Neurodegenerative diseases are challenging for systems biology because of the lack of reliable animal models or patient samples at early disease stages. Induced pluripotent stem cells (iPSCs) could address these challenges. We investigated DNA, RNA, epigenetics, and proteins in iPSC-derived motor neurons from patients with ALS carrying hexanucleotide expansions in C9ORF72. Using integrative computational methods combining all omics datasets, we identified novel and known dysregulated pathways. We used a C9ORF72 Drosophila model to distinguish pathways contributing to disease phenotypes from compensatory ones and confirmed alterations in some pathways in postmortem spinal cord tissue of patients with ALS. A different differentiation protocol was used to derive a separate set of C9ORF72 and control motor neurons. Many individual -omics differed by protocol, but some core dysregulated pathways were consistent. This strategy of analyzing patient-specific neurons provides disease-related outcomes with small numbers of heterogeneous lines and reduces variation from single-omics to elucidate network-based signatures.

Generation of two human induced pluripotent stem cell lines from fibroblasts of unrelated Parkinson's patients carrying the G2019S mutation in the LRRK2 gene (LCSBi005, LCSBi006).

Mutations in the LRRK2 gene are known to mediate predisposition to Parkinson disease. Fibroblasts heterozygous for the G2019S LRRK2 mutation were obtained from a 53-year-old male patient with disease onset at 34 years (LCSBi005, ND29542), and from a 63-year-old male patient with disease onset at 56 years (LCSBi006, ND34267). Induced pluripotent stem cell (iPSC) clones were generated for each cell line using Sendai virus. The absence of chromosomal defects was confirmed using array comparative genomic hybridization. The cell lines express pluripotency markers and have the ability to differentiate into all three germ layers.

Generation of two human induced pluripotent stem cell lines (iPSCs) with mutations of the α-synuclein (SNCA) gene associated with Parkinson's disease; the A53T mutation (LCSBi003) and a triplication of the SNCA gene (LCSBi007).

Mutations in the SNCA (α-synuclein, PARK1) gene significantly contribute to Parkinson's disease and SNCA inclusions are strongly associated with PD. Fibroblasts from a 51-year-old female patient with disease onset at 39 years, carrying the A53T SNCA mutation (LCSBi003, ND40996), and fibroblasts with a triplication of the SNCA gene obtained from a 55-year-old female patient with disease onset at 52 years (LCSBi007, ND27760), were reprogrammed into human induced pluripotent stem cells (iPSCs) using Sendai virus. The presence of other genetic variants was determined using array comparative genomic hybridization. Presence of SNCA triplication was confirmed by FISH analysis.

Genetically encoded cell-death indicators (GEDI) to detect an early irreversible commitment to neurodegeneration.

Cell death is a critical process that occurs normally in health and disease. However, its study is limited due to available technologies that only detect very late stages in the process or specific death mechanisms. Here, we report the development of a family of fluorescent biosensors called genetically encoded death indicators (GEDIs). GEDIs specifically detect an intracellular Ca2+ level that cells achieve early in the cell death process and that marks a stage at which cells are irreversibly committed to die. The time-resolved nature of a GEDI delineates a binary demarcation of cell life and death in real time, reformulating the definition of cell death. We demonstrate that GEDIs acutely and accurately report death of rodent and human neurons in vitro, and show that GEDIs enable an automated imaging platform for single cell detection of neuronal death in vivo in zebrafish larvae. With a quantitative pseudo-ratiometric signal, GEDIs facilitate high-throughput analysis of cell death in time-lapse imaging analysis, providing the necessary resolution and scale to identify early factors leading to cell death in studies of neurodegeneration.