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The complexity of affected brain regions and cell types is a challenge for Huntington’s disease (HD) treatment. Here we use single nucleus RNA sequencing to investigate molecular pathology in the cortex and striatum from R6/2 mice and human HD post-mortem tissue. We identify cell type-specific and -agnostic signatures suggesting oligodendrocytes (OLs) and oligodendrocyte precursors (OPCs) are arrested in intermediate maturation states. OL-lineage regulators OLIG1 and OLIG2 are negatively correlated with CAG length in human OPCs, and ATACseq analysis of HD mouse NeuN-negative cells shows decreased accessibility regulated by OL maturation genes. The data implicates glucose and lipid metabolism in abnormal cell maturation and identify PRKCE and Thiamine Pyrophosphokinase 1 ( TPK1 ) as central genes. Thiamine/biotin treatment of R6/1 HD mice to compensate for TPK1 dysregulation restores OL maturation and rescues neuronal pathology. Our insights into HD OL pathology spans multiple brain regions and link OL maturation deficits to abnormal thiamine metabolism. Here the authors evaluate single cell gene expression from mouse and human Huntington’s disease brains, finding incomplete oligodendrocyte maturation and pathways involved. Treating mice with thiamine/biotin ameliorates molecular pathology.

Many genes that drive normal cellular development also contribute to oncogenesis. Medulloblastoma (MB) tumors likely arise from neuronal progenitors in the cerebellum, and we hypothesized that the heterogeneity observed in MBs with sonic hedgehog (SHH) activation could be due to differences in developmental pathways. To investigate this question, here we perform single-nucleus RNA sequencing on highly differentiated SHH MBs with extensively nodular histology and observed malignant cells resembling each stage of canonical granule neuron development. Through innovative computational approaches, we connect these results to published datasets and find that some established molecular subtypes of SHH MB appear arrested at different developmental stages. Additionally, using multiplexed proteomic imaging and MALDI imaging mass spectrometry, we identify distinct histological and metabolic profiles for highly differentiated tumors. Our approaches are applicable to understanding the interplay between heterogeneity and differentiation in other cancers and can provide important insights for the design of targeted therapies. The role of developmental pathways in medulloblastoma tumours (MB) with sonic hedgehog (SHH) activation remains to be explored. Here, the authors perform multi-omic analysis and characterise the key transcriptomic and metabolic patterns of highly differentiated cells in SHH MBs.

Medulloblastomas with extensive nodularity are cerebellar tumors characterized by two distinct compartments and variable disease progression. The mechanisms governing the balance between proliferation and differentiation in MBEN remain poorly understood. Here, we employ a multi-modal single cell transcriptome analysis to dissect this process. In the internodular compartment, we identify proliferating cerebellar granular neuronal precursor-like malignant cells, along with stromal, vascular, and immune cells. In contrast, the nodular compartment comprises postmitotic, neuronally differentiated malignant cells. Both compartments are connected through an intermediate cell stage resembling actively migrating CGNPs. Notably, we also discover astrocytic-like malignant cells, found in proximity to migrating and differentiated cells at the transition zone between the two compartments. Our study sheds light on the spatial tissue organization and its link to the developmental trajectory, resulting in a more benign tumor phenotype. This integrative approach holds promise to explore intercompartmental interactions in other cancers with varying histology. The mechanisms regulating the balance between proliferation and differentiation in medulloblastomas with extensive nodularity (MBEN) remain poorly understood. Here, single cell multi-omics and spatial analysis characterises the spatial tissue organisation of MBEN in the context of the developmental trajectory.

BackgroundVentricular dyssynchrony and its relationship to clinical outcomes is not well characterized in patients following Fontan palliation.MethodsSingle-center retrospective analysis of cardiac magnetic resonance (CMR) imaging of patients with a Fontan circulation and an age-matched healthy comparison cohort as controls. Feature tracking was performed on all slices of a ventricular short-axis cine stack. Circumferential and radial strain, strain rate, and displacement were measured; and multiple dyssynchrony metrics were calculated based on timing of these measurements (including standard deviation of time-to-peak, maximum opposing wall delay, and maximum base-to-apex delay). Primary endpoint was a composite measure including time to death, heart transplant or heart transplant listing (D/HTx).ResultsA total of 503 cases (15 y; IQR 10, 21) and 42 controls (16 y; IQR 11, 20) were analyzed. Compared to controls, Fontan patients had increased dyssynchrony metrics, longer QRS duration, larger ventricular volumes, and worse systolic function. Dyssynchrony metrics were higher in patients with right ventricular (RV) or mixed morphology compared to those with LV morphology. At median follow-up of 4.3 years, 11% had D/HTx. Multiple risk factors for D/HTx were identified, including RV morphology, ventricular dilation, dysfunction, QRS prolongation, and dyssynchrony. Ventricular dilation and RV morphology were independently associated with D/HTx.ConclusionsCompared to control LVs, single right and mixed morphology ventricles in the Fontan circulation exhibit a higher degree of mechanical dyssynchrony as evaluated by CMR-FT. Dyssynchrony indices correlate with ventricular size and function and are associated with death or need for heart transplantation. These data add to the growing understanding regarding factors that can be used to risk-stratify patients with the Fontan circulation.

Quantum emitter-based schemes for the generation of photonic graph states offer a promising, resource-efficient methodology for realizing distributed quantum computation and communication protocols on near-term hardware. We present a heralded scheme for making photonic graph states that is compatible with the typically poor photon collection from state-of-the-art coherent quantum emitters. We demonstrate that the construction time for large graph states can be polynomial in the photon collection efficiency, as compared to the exponential scaling of current emitter-based schemes, which assume deterministic photon collection. The additional overhead here consists of an extra spin qubit plus one additional spin-spin entangling gate per photon added to the graph. While the proposed scheme requires both non-demolition measurement and efficient storage of photons in order to generate graph states for arbitrary applications, we show that many useful tasks, including measurement-based quantum computation, can be implemented without these requirements. As a use case of our scheme, we construct a protocol for secure two-party computation that can be implemented efficiently on current hardware. Estimates of the fidelity to produce graph states used in the computation are given assuming current and near-term fidelities for highly coherent quantum emitters.

The Alu element, the most prevalent SINE (short interspersed element) in the human genome, is one of the many RNA-encoding genes that evolved from the 7SL RNA gene. During analysis of the evolution of 7SL-derived RNAs, two distinct evolutionary intermediates capable of self-catalyzed DNA depurination (SDP) were identified. These SDP sequences spontaneously create apurinic sites that can result in increased mutagenesis due to their error-prone repair. This DNA self-depurination mechanism has been shown both in vitro and in vivo to lead to substitution and short frameshift mutations at a frequency that far exceeds their occurrence due to random errors in DNA replication. In both evolutionary intermediates, the same self-depurination sequence overlaps motifs necessary for successful transcription and SRP9/14 (signal recognition particle) binding; hence, mutations in this region could disrupt RNA activity. Yet, the 7SL-derived RNAs that arose from the elements capable of SDP show significant diversity in this region, and every new sequence retains the transcription and SRP9/14-binding motifs, even as it has lost the SDP sequence. While some (but not all) of the mutagenesis can be alternatively attributed to CpG decay, the very fact that the self-depurinating sequences are selectively discarded in all cases suggests that this was evolutionarily motivated to prevent further destructive mutagenesis by the SDP mechanism.

Neurological conditions affect the brain and other parts of the nervous system. This includes neurodegenerative diseases like Huntington’s Disease, psychiatric conditions like schizophrenia, and brain cancers like glioblastoma. These conditions are particularly challenging to study because they affect such a vital and complex organ system, making it difficult to understand disease etiology and to develop high-quality model systems.Because of these challenges, experiments studying neurological diseases typically either contain very few patient samples or are collected from imperfect model systems. Machine learning approaches have proven helpful for processing these types of datasets and identifying relevant biological signal. In this thesis, I detail five examples of the utility of machine learning methods for analyzing neurological disease data. Some chapters focus primarily on the development of novel machine learning methods, while others discuss the implementation of established algorithms leading to significant advancements in our understanding of the given disease.Chapter 2 details a novel gene set scoring algorithm that significantly improves upon existing methods. This new approach is particularly useful for analyzing single-cell transcriptomics assays, which are becoming increasingly common in neurological disease studies. In Chapter 3, I describe how multi-omic integration of ATAC-Seq, ChIP-Seq, and RNA-seq data revealed a novel population of cycling cells relevant to Huntington’s Disease models. In Chapter 4, I discuss an improved multi-commodity flow algorithm for omics data integration and highlight its utility for understanding drug effects in glioblastoma. Chapter 5 highlights how clustering and the Prize-Collecting Steiner Forest algorithm led to a better understanding of proteomic subtypes in medulloblastoma tumors. Lastly, Chapter 6 expands upon the work in Chapter 5, and details how I used computational approaches to figure out that some medulloblastoma tumors contain cells recapitulating cerebellar granule neuron development.In summary, this thesis showcases the value machine learning techniques for analyzing the small, complicated datasets typically found in neurological disease experiments. Throughout this work, I emphasize the importance of collecting and integrating multiple types of biological data to get a more complete understanding of these conditions.

An efficient paradigm for multi-party computation (MPC) are protocols structured around access to shared pre-processed computational resources. In this model, certain forms of correlated randomness are distributed to the participants prior to their computation. The shared randomness is then consumed in a computation phase that involves public communication with efficient round complexity, and the computation is secure in this second phase provided the initial correlations were distributed securely. Usually the latter requires some strong setup assumptions, such as a trusted dealer and private channels. We present a novel approach for generating these correlations from entangled quantum graph states and yield information-theoretic privacy guarantees that hold against a malicious adversary, with limited assumptions. Our primary contribution is a tripartite resource state and measurement-based protocol for extracting a binary multiplication triple, a special form of shared randomness that enables the private multiplication of a bit conjunction. Here, we employ a third party as a Referee and demand only an honest pair among the three parties. The role of this Referee is weaker than that of a Dealer, as the Referee learns nothing about the underlying shared randomness that is disseminated. We prove perfect privacy for our protocol, assuming access to an ideal copy of the resource state, an assumption that is based on the existence of graph state verification protocols. Finally, we demonstrate its application as a primitive for more complex Boolean functionalities such as 1-out-of-2 oblivious transfer (OT) and MPC for an arbitrary \\(N\\)-party Boolean function, assuming access to the proper broadcasting channel.

The complexity of affected brain regions and cell types is a challenge for Huntington disease (HD) treatment. Here we used single nucleus RNA sequencing (snRNAseq) to investigate mechanism of pathology in the cortex and striatum from R6/2 mice at 8 and 12w and in three regions of human HD post-mortem tissue. We identified cell type-specific and cell agnostic signatures and found changes suggesting oligodendrocytes (OLs) and oligodendrocyte precursors (OPCs) were arrested in intermediate maturation states. OL-lineage regulators OLIG1 and OLIG2 were negatively correlated with CAG length in human OPCs, and ATACseq analysis of HD mouse NeuN-negative cells showed decreased accessibility of sites regulated by OL maturation genes. Glucose and lipid metabolism were implicated in abnormal cell maturation and PRKCE and Thiamine Pyrophosphokinase 1 were identified as central genes. High dose thiamine/biotin treatment of R6/1 HD mice to target thiamine metabolism not only restored OL maturation, but also rescued pathology in neurons. These findings reveal insights into HD OL pathology that spans multiple brain regions and link OL maturation deficits to abnormal thiamine metabolism. Competing Interest Statement The authors have declared no competing interest.