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Additional file 3: Figure S2. In vitro microglia networks are disrupted and dissimilar to the freshly isolated network. (a) Transcriptional regulatory network for astrocyte-plated in vitro cells inferred by MEGENA. Top 50 nodes are labelled, and nodes are colored corresponding to the 5 core modules identified by MEGENA clustering analysis. (b) Bar graph of the top 40 nodes shows from a relative strength between nodes. (c) Heatmap of the scaled expression of the top 75 genes of each core module across 2000 downsampled cells. Modules strongly associate with specific clusters. Module 9 contains genes highly expressed in the macrophage-like cluster. Module 5 contains genes associated with the microglia-like 1, and monocyte-like 1 and 2 clusters. Module 6 contains hubs of ribosomal genes and is downregulated in microglia-like cluster 2 and upregulated in microglia-like cluster 3. Modules 7 and 8 contain cell cycle genes associated with proliferation cluster. (d) Subnetwork of module 5, a heterogenous module that contains a variety of hubs. Hubs like Cx3cr1, Fcrls, and Fcer1g are markers of resting microglia, while hubs like Apoe, Lgals1, Lgals3 are activation markers. Smaller hubs like Anxa1 and S100a4 are unique to monocyte-like cells. The weighted module expression of this module is strongest in the microglia-like 1 cluster, but expression of this module is robust across all clusters. Metascape GO enrichment shows enrichment for immune and cell death processes. (e) Subnetwork of module 7, a cell cycle module whose module weighted expression is significantly higher in proliferation cluster 6 (two-tailed t-test, p-value = 1.62e-166). Metascape analysis of this clusters shows enrichment of cell cycle processes. (f) Subnetwork of module 9, with hubs Mrc1 and Stab1 that are unique markers of macrophages. Weighted module expression of this module is expressed robustly in all clusters, but is highest in macrophage-like cluster 0 (two-tailed t-test, p-value = 8.23e-7). Metascape GO enrichment shows this module is related to lysosomal and vesicle transport pathways.

Additional file 4: Figure S3. Key drivers of the cultured microglia phenotype. Top 100 of 444 total key drivers with prioritized rank derived from the astro-plated microglia and micro-isolated microglia networks.

Additional file 1: Table S1. Genes differentially expressed in each cluster of primary cultured astrocyte-plated microglial cells. All genes displayed have an adjusted p value of less than 0.05 and are expressed in a minimum of 25% of cells in each cluster.

Additional file 2: Figure S1. MEGENA network analysis of freshly isolated cells establishes a reference transcriptional network for microglia. (a) Transcriptional regulatory network for freshly isolated microglia inferred by MEGENA. Nodes represent genes, and an edge between nodes represents the significant correlation of those two genes. Bar graph shows relative strength of these top 20 nodes and highlights the module to which each gene belongs. Heatmap shows the scaled expression of the top 100 genes of each of the 3 core MEGENA modules. Heatmap is represented by a downsampled 2000 of the freshly isolated cells. (b) Subnetwork of module 2. Weighted module expression is calculated by calculating the sum of the expression of each gene in the module, weighted by its strength in the network, and signed by whether that gene has positive or negative correlation with the largest hub of that module. Plotting the module weighted sum in a violin and feature plot shows strong, significant association of module 2 with the activated cluster (two-tailed t-test between activated vs homeostatic cluster, p-value = 1.93e-15). Metascape gene ontology (GO) enrichment suggests that this module is related to inflammatory response and the transcriptional response to stress. (c) Subnetwork of module 3, containing many homeostatic microglia markers. Weighted module expression is highest in the homeostatic cluster and significantly decreases in cells from the activated cluster (two-tailed t-test between activated vs homeostatic cluster, p-value = 4.34e-60). Metascape shows that pathways enriched for this module include ribonucleoprotein complex formation, functioning of the lysosome, and immune pathways like neutrophil degranulation and myeloid activation. (d) Network of the subnetwork for module 4, which contains many interferon-related genes. Module weighted sum is significantly increased in the interferon cluster compared to the homeostatic cluster (two-tailed t-test, p-value = 7.65e-23). Metascape enrichment analysis shows pathways related to interferon signaling and cytokine production.

Additional file 1. Table S1 Neuropathological information of samples used in the histological study. Table S2 Demographics and neuropathological characteristics of human subjects. Figure S1 CLU is present in tau aggregates and is upregulated in AAV-TauP301L animals. a Brain tissues of 6-month-old wild-type (WT) mice injected with AAV-GFP (Ctrl) and AAV-TauP301L. Arrows show CLU co-localization with tau deposits. Arrowheads represent tau tangles without CLU immunoreactivity. Scale bar, 100 μm. b Quantification of CLU protein levels in cortex of WT mice injected with AAV-GFP (Ctrl) and AAV-TauP301. N = 15–16 mice/group. Data presented as mean ± S.E.M. and analyzed with Student’s t test, *p

Rare structural variants (SVs) - insertions, deletions, and complex rearrangements - can cause Mendelian disease, yet they remain difficult to accurately detect and interpret. We sequenced and analyzed Oxford Nanopore long-read genomes of 68 individuals from the Undiagnosed Disease Network (UDN) with no previously identified diagnostic mutations from short-read sequencing. Using our optimized SV detection pipelines and 571 control long-read genomes, we detected 716 long-read rare (MAF < 0.01) SV alleles per genome on average, achieving a 2.4x increase from short-reads. To characterize the functional effects of rare SVs, we assessed their relationship with gene expression from blood or fibroblasts from the same individuals, and found that rare SVs overlapping enhancers were enriched (LOR = 0.46) near expression outliers. We also evaluated tandem repeat expansions (TREs) and found 14 rare TREs per genome; notably these TREs were also enriched near overexpression outliers. To prioritize candidate functional SVs, we developed Watershed-SV, a probabilistic model that integrates expression data with SV-specific genomic annotations, which significantly outperforms baseline models that don't incorporate expression data. Watershed-SV identified a median of eight high-confidence functional SVs per UDN genome. Notably, this included compound heterozygous deletions in FAM177A1 shared by two siblings, which were likely causal for a rare neurodevelopmental disorder. Our observations demonstrate the promise of integrating long-read sequencing with gene expression towards improving the prioritization of functional SVs and TREs in rare disease patients., Competing Interests: COMPETING INTEREST STATEMENT SBM is an advisor to BioMarin, Myome and Tenaya Therapeutics. AB is a co-founder of CellCipher, Inc, is a shareholder in Alphabet, Inc, and has consulted for Third Rock Ventures, LLC. EAA is the founder of Personalis, Deepcell, Svexa, RCD Co, Parameter Health, an advisor for SequenceBio, Foresite Labs, PacBio, a non-executive director at AstraZeneca, hold stocks in Oxford Nanopore, Pacific Biosciences, AstraZeneca, and offers collaborative support in kind to Illumina, Pacific Biosciences, Oxford Nanopore

Less than half of individuals with a suspected Mendelian condition receive a precise molecular diagnosis after comprehensive clinical genetic testing. Improvements in data quality and costs have heightened interest in using long-read sequencing (LRS) to streamline clinical genomic testing, but the absence of control datasets for variant filtering and prioritization has made tertiary analysis of LRS data challenging. To address this, the 1000 Genomes Project ONT Sequencing Consortium aims to generate LRS data from at least 800 of the 1000 Genomes Project samples. Our goal is to use LRS to identify a broader spectrum of variation so we may improve our understanding of normal patterns of human variation. Here, we present data from analysis of the first 100 samples, representing all 5 superpopulations and 19 subpopulations. These samples, sequenced to an average depth of coverage of 37x and sequence read N50 of 54 kbp, have high concordance with previous studies for identifying single nucleotide and indel variants outside of homopolymer regions. Using multiple structural variant (SV) callers, we identify an average of 24,543 high-confidence SVs per genome, including shared and private SVs likely to disrupt gene function as well as pathogenic expansions within disease-associated repeats that were not detected using short reads. Evaluation of methylation signatures revealed expected patterns at known imprinted loci, samples with skewed X-inactivation patterns, and novel differentially methylated regions. All raw sequencing data, processed data, and summary statistics are publicly available, providing a valuable resource for the clinical genetics community to discover pathogenic SVs., Competing Interests: COMPETING INTEREST STATEMENT WDC, ML, FS, and DEM have received research support and/or consumables from ONT. WDC, JG, FS, and DEM have received travel funding to speak on behalf of ONT. DEM is on a scientific advisory board at ONT. FS has received research support from Illumina, Genetech, and PacBio. SBM is an advisor to BioMarin, MyOme, and Tenaya Therapeutics. EEE is a scientific advisory board (SAB) member of Variant Bio, Inc. DEM holds stock options in MyOme.

Transcriptomics is a powerful tool for unraveling the molecular effects of genetic variants and disease diagnosis. Prior studies have demonstrated that choice of genome build impacts variant interpretation and diagnostic yield for genomic analyses. To identify the extent genome build also impacts transcriptomics analyses, we studied the effect of the hg19, hg38, and CHM13 genome builds on expression quantification and outlier detection in 386 rare disease and familial control samples from both the Undiagnosed Diseases Network (UDN) and Genomics Research to Elucidate the Genetics of Rare Disease (GREGoR) Consortium. We identified 2,800 genes with build-dependent quantification across six routinely-collected biospecimens, including 1,391 protein-coding genes and 341 known rare disease genes. We further observed multiple genes that only have detectable expression in a subset of genome builds. Finally, we characterized how genome build impacts the detection of outlier transcriptomic events. Combined, we provide a database of genes impacted by build choice, and recommend that transcriptomics-guided analyses and diagnoses are cross-referenced with these data for robustness., Competing Interests: Declaration of interests During this project R.A.U. was employed for an internship by Vertex Pharmaceuticals. P.C.G. is a consultant for BioMarin. S.B.M. is an advisor to BioMarin, MyOme, and Tenaya Therapeutics.