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PubMed2025

PoweREST: Statistical power estimation for spatial transcriptomics experiments to detect differentially expressed genes between two conditions.

Recent advancements in spatial transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost for current ST data generation techniques restricts the large-scale application of ST. Consequently, maximization of the use of available resources to achieve robust statistical power for ST data is a pressing need. One fundamental question in ST analysis is detection of differentially expressed genes (DEGs) under different conditions using ST data. Such DEG analyses are performed frequently, but their power calculations are rarely discussed in the literature. To address this gap, we developed PoweREST, a power estimation tool designed to support the power calculation for DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments and after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application that allows users to interactively calculate and visualize study power along with relevant parameters.

Gene Expression Profiling↗

PubMed2025

Personalizing chemotherapy drug selection using a novel transcriptomic chemogram.

Gene signatures predictive of chemotherapeutic response have the potential to extend the reach of precision medicine by allowing oncologists to optimize treatment for individuals. Most published predictive signatures are only capable of predicting response for individual drugs, but most chemotherapy regimens utilize combinations of different agents. We propose a unified framework, called the chemogram, that uses predictive signatures to rank the relative predicted sensitivity of different drugs for individual tumors. Using this approach, providers could efficiently screen against many therapeutics to optimize chemotherapy at any time, whether it be for a treatment-naive tumor or a chemo-resistant tumor requiring a new treatment strategy. To demonstrate the utility of the chemogram, we used predictive signatures (extracted from a previously established method) in our framework to rank predicted sensitivity among drugs within cell lines. We then compared the rank order of predicted and observed response against each drug. Across most cancer types, chemogram-generated predictions were more accurate than predictions made by randomly generated gene signatures, signatures extracted from differential expression alone, and was comparable to another established method of drug response prediction. Our framework demonstrates the ability of transcriptomic signatures to not only predict chemotherapeutic response, but also correctly assign rankings of drug sensitivity on an individual basis. Additionally, scaling the chemogram to include more drugs does not compromise accuracy.

Humans↗

PubMed2025

Transcriptome-wide root causal inference.

Root causal genes correspond to the first gene expression levels perturbed during pathogenesis by genetic or non-genetic factors. Targeting root causal genes has the potential to alleviate disease entirely by eliminating pathology near its onset. No existing algorithm has been designed to discover root causal genes from observational data alone. We therefore propose the Transcriptome-Wide Root Causal Inference (TWRCI) algorithm that identifies root causal genes and their causal graph using a combination of genetic variant and unperturbed bulk RNA sequencing data. TWRCI uses a novel competitive regression procedure to annotate cis and trans-genetic variants to the gene expression levels they directly cause. The algorithm simultaneously determines the sequence in which gene expression changes propagate through the system to pinpoint the underlying causal graph and estimate root causal effects. TWRCI outperforms alternative approaches across a diverse group of metrics by directly targeting root causal genes while accounting for distal relations, linkage disequilibrium, patient heterogeneity and widespread pleiotropy. We demonstrate the algorithm by uncovering the root causal mechanisms of two complex diseases, which we confirm by replication using independent genome-wide summary statistics.

Algorithms↗

PubMed2026

Ten quick tips for spatial transcriptomics analysis.

Spatial transcriptomics (ST) enables genome-wide gene expression profiling while retaining spatial context within tissue sections. Since the foundational work by Ståhl et al. in 2016, the field has expanded rapidly, with diverse platforms now spanning sequencing-based (e.g., Visium, Visium HD, Slide-seq, Stereo-seq, and Seq-Scope) and imaging-based (e.g., MERFISH, Xenium, and CosMx SMI) approaches. The breadth of platforms, data structures, and computational tools, however, can be daunting for newcomers. Here, we present ten quick tips spanning the entire ST research workflow: whether ST suits a given biological question, how to select a platform aligned with study objectives, how to understand and process ST data, and which software tools to employ for analysis and visualization. We further discuss interpreting spatial patterns in biological context, integrating complementary modalities such as single-cell RNA sequencing and spatial proteomics, and leveraging public datasets and sharing results. Finally, we highlight current limitations of ST, particularly the challenge of reconstructing three-dimensional tissue architecture from serial tissue sections. This review provides biologists, bioinformaticians, and clinician-scientists with a concise, platform-neutral roadmap for incorporating ST into research, from experimental design to biological discovery.

Spatial Transcriptomics↗

PubMed2026

Characterization of METTL3/14-mediated m6A modification in human transcriptome using Nanopore direct RNA sequencing.

Post-transcriptional RNA modifications modulate diverse aspects of RNA metabolism. N6-methyladenosine (m6A), one of the most abundant internal RNA modifications, is deposited by the core methyltransferase complex, METTL3 and METTL14. Oxford Nanopore Technologies (ONT) platform permits direct, single RNA molecule sequencing while preserving native modifications. However, without rigorous benchmarking, the accuracy and reproducibility of modification detection remain uncertain. Here, we leveraged ONT to comprehensively profile bona fide m6A modifications in cellular RNAs at single-nucleotide resolution by integrating two direct RNA sequencing chemistries (RNA002 and RNA004) with the m6Anet and Dorado modification-detection models. We independently depleted METTL3 and METTL14 in human cells and rigorously validated modification calls through several assays and independent orthogonal methods (GLORI and miCLIP). We find that Dorado detected a higher number of m6A events and enabled simultaneous detection of other RNA modifications (5-methylcytosine, pseudouridine, and inosine). Pairing Dorado with an in vitro transcribed, unmodified control under stringent filtering, we provide compelling evidence supporting a global reduction in m6A sites and stoichiometry within coding sequences and across genes, particularly in highly modified genes and sites, and at consensus DRACH motifs. We report a differential and complex regulation of modified transcripts, accompanied by a global reduction in poly(A) tail length. Notably, METTL3 and METTL14 depletion produced distinct transcript-specific effects, supporting non-redundant roles within the m6A writer complex. Together, our study illustrates a notable advancement of ONT capabilities and establishes a robust transcriptome-wide framework for RNA modification detection, thereby laying the groundwork for exploring the contribution of METTL3/METTL14 to cellular functions and disease.

Humans↗

PubMed2025

Venomics of the Arabian saw-scaled viper (Echis coloratus) through transcriptome-guided proteomics and in vitro functional profiling.

The Arabian saw-scaled viper (Echis coloratus) is among the snakes of highest medical relevance in the Middle East and North Africa. However, to date, its venom has been investigated in a very limited number of studies, and much remains unknown regarding its compositional and functional properties. By integrating proteotranscriptomics with bioactivity profiling, we present a comprehensive transcriptome-level catalogue of E. coloratus venom components and their associated biological activities. Our analysis identified 183 venom components belonging to 17 distinct protein families. Relative toxin abundances revealed that 92% of the venom proteome is composed of C-type lectin and C-type lectin-related protein (CTL), L-amino acid oxidase (LAAO), phospholipase A2 (PLA2), snake venom serine protease (SVSP), and snake venom metalloproteinase (SVMP), with CTL and PLA2 alone accounting for 73% of the total composition. Bioassays targeting key aspects of viperid envenomation demonstrated potent protease and PLA2 activity in a concentration-dependent manner. In contrast, Factor Xa-like, plasmin-like, and haemolytic activities were negligible. Marked cytotoxicity was observed at the highest concentration tested (i.e., 25 μg/ml) in the mammalian cell lines MDCK II and Calu-3, whereas cytotoxic effects were minimal at lower concentrations. These findings highlight the complexity and potency of E. coloratus venom, and provide a valuable foundation for improving our understanding of envenomation caused by this species.

Animals↗

PubMed2025

Genome-wide DNA methylation and transcriptome sequencing analyses of lens tissue in an age-related mouse cataract model.

DNA methylation is known to be associated with cataracts. In this study, we used a mouse model and performed DNA methylation and transcriptome sequencing analyses to find epigenetic indicators for age-related cataracts (ARC). Anterior lens capsule membrane tissues from young and aged mice were analyzed by MethylRAD-seq to detect the genome-wide methylation of extracted DNA. The young and aged mice had 76,524 and 15,608 differentially methylated CCGG and CCWGG sites, respectively. The Pearson correlation analysis detected 109 and 33 differentially expressed genes (DEGs) with negative methylation at CCGG and CCWGG sites, respectively, in their promoter regions. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses showed that DEGs with abnormal methylation at CCGG sites were primarily associated with protein kinase C signaling (Akap12, Capzb), protein threonine kinase activity (Dmpk, Mapkapk3), and calcium signaling pathway (Slc25a4, Cacna1f), whereas DEGs with abnormal methylation at CCWGG sites were associated with ribosomal protein S6 kinase activity (Rps6ka3). These genes were validated by pyrosequencing methylation analysis. The results showed that the ARC group (aged mice) had lower Dmpk and Slc25a4 methylation levels and a higher Rps6ka3 methylation than the control group (young mice), which is consistent with the results of the joint analysis of differentially methylated and differentially expressed genes. In conclusion, we confirmed the genome-wide DNA methylation pattern and gene expression profile of ARC based on the mouse cataract model with aged mice. The identified methylation molecular markers have great potential for application in the future diagnosis and treatment of ARC.

Animals↗

PubMed2025

Transcriptomic profile induced by calcitriol in CaSki human cervical cancer cell line.

The vitamin D endocrine system, primarily mediated by its main metabolite calcitriol and the vitamin D receptor (VDR), plays a critical role in numerous human physiological processes, ranging from calcium metabolism to the prevention of various tumors, including cervical cancer. In this study, we comprehensively investigated the genomic regulatory effects of calcitriol in a cervical cancer model. We examined the transcriptional changes induced by calcitriol in CaSki cells, a cervical cell line harboring multiple copies of HPV16, the primary causal agent of cervical cancer. Our microarray findings, revealed that calcitriol regulated over 1000 protein-coding genes, exhibiting a predominantly repressive effect on the CaSki cell transcriptome by suppressing twice as many genes as it induced. Calcitriol decreased EPHA2 and RARA expression while inducing KLK6 and CYP4F3 expression in CaSki cells, as validated by qPCR and Western blot. Functional analysis demonstrated that calcitriol effectively inhibited key processes involved in cancer progression, including cell proliferation and migration. This was further supported by the significant downregulation of MMP7 and MMP13 mRNA levels. Our microarray results also showed that, in addition to its effects on protein-coding genes, calcitriol significantly regulates non-coding RNAs, altering the expression of approximately 400 non-coding RNAs, including 111 microRNA precursors and 29 mature microRNAs, of which 17 were upregulated and 12 downregulated. Notably, among these calcitriol-regulated microRNAs are some involved in cervical cancer biology, such as miR-6129, miR-382, miR-655, miR-211, miR-590, miR-130a, miR-301a, and miR-1252. Collectively, these findings suggest that calcitriol exhibits a significant antitumor effect in this advanced cervical cancer model by blocking critical processes for tumor progression, underscoring the importance of maintaining adequate vitamin D nutritional status.

Humans↗

PubMed2026

Transcriptomic and proteomic signatures following AS03-adjuvanted Influenza A/H7N9 vaccine.

INTRODUCTION: Vaccines targeting avian influenza virus A/H7N9 are poorly immunogenic. While the immune responses can be improved with oil-in-water emulsion adjuvants such as Adjuvant System 03 (AS03), the cellular mechanisms underpinning the adjuvant effect are incompletely characterized and poorly understood. METHODS: We enrolled 30 healthy adult participants and used RNA sequencing and quantitative proteomics to characterize the response to two doses of the influenza A/H7N9 vaccine, with and without AS03, in six immune cell types. These responses were compared to those seen after administration of an unadjuvanted seasonal in uenza A/H3N2 variant vaccine to identify signatures unique to adjuvanted influenza vaccines and correlated with later antibody responses. Transcriptomic and proteomic analyses revealed that. RESULTS: AS03-adjuvanted vaccine was associated with upregulation of immune pathways in innate immune cells within 24h following vaccination for phagocytosis, antigen presentation and processing, inflammasome activation, NK-cell mediated cytotoxicity, IgA production, and interferon-response pathways. Moreover, while major histocompatibility complex (MHC I and II) upregulation was observed across multiple immune cell types, MHCII gene transcription was also increased in the neutrophil compartment, generating the hypothesis that neutrophils may play a more important role in antigen presentation than previously understood. DISCUSSION: Taken together, these data provide a more complete mechanistic understanding of oil-in-water adjuvants and their role in enhancing the immune response for pandemic influenza preparedness. CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/study/NCT02921997?term=NCT02921997&viewType, idientifier NCT02921997.

Adult↗

PubMed2026

Primary Tumor Epigenetic and Transcriptomic Alterations Associated with Nodal Burden and Metastatic Risk in ER+/HER2- Breast Cancer.

De-escalation of axillary surgery has resulted in the loss of pathologic nodal information, yet the extent of lymph node involvement remains an important determinant of treatment decisions in estrogen receptor-positive (ER+)/HER2- disease. We examined whether primary tumors differed molecularly according to the extent of this regional dissemination. Genome-wide DNA methylation profiling of primary ER+/HER2- tumors from 47 patients with pN1 (n = 29) vs. >pN1 (n = 18) disease showed differences concentrated at promoters of developmental and cell-adhesion genes. By integrating methylomes with transcriptomes from the TCGA-BRCA cohort (n = 148) and clinical outcomes from KM Plotter (RFS, n = 1154; OS, n = 442; DMFS, n = 423), we identified four genes (ARL10, RIC3, CXCL14, KCNH2) showing concordant molecular and clinical associations, from which we derived the Lymph-node Involvement Outcome Numerator (LION) score. Lower LION scores were observed in metastatic lesions from the AURORA US cohort (n = 45). In SCAN-B (n = 3969), lower scores were associated with shorter distant recurrence-free intervals (HR = 0.38; 95% CI 0.23-0.62); this association persisted after adjustment for age, nodal and tumor category but was lost after adjustment for histological grade (HR = 0.83; 95% CI 0.48-1.44), indicating that the score and grade capture overlapping biology. These findings suggest that primary tumors already display coordinated epigenetic and transcriptional alterations associated with the extent of metastatic dissemination.

Humans↗

PubMed2026

A Practical Workflow for Spatial Transcriptomics Data Analysis: From Data Acquisition to Advanced Analyses.

Spatial transcriptomics (ST) profiles genome-wide gene expression while preserving the two-dimensional spatial context of mRNA molecules within tissue sections, enabling studies of tissue architecture and microenvironment-associated biology. However, ST analysis remains challenging because data import, quality control, integration, deconvolution, spatial statistics, and visualization often require multiple software environments and reproducible parameter choices. This protocol presents a practical computational workflow for public ST datasets in R, beginning with data acquisition and software setup and proceeding through Seurat-based data loading, quality control, normalization, multi-sample integration, clustering, and spatially variable gene analysis. The workflow then applies complementary deconvolution strategies, including reference-guided SPOTlight analysis and unsupervised STdeconvolve topic modeling, followed by Giotto-based spatial cell-cell communication analysis and interactive region-of-interest (ROI) selection using a custom Python Dash application. By emphasizing script-based execution, explicit parameter rationales, expected outputs, and troubleshooting checkpoints, the protocol provides an adaptable framework for standard array-based ST datasets and related platforms after dataset- and platform-specific parameter evaluation.

Spatial Transcriptomics↗

PubMed2026

Multistage Genetic, Transcriptomic, and Single-Cell Evidence Prioritizes MAP1LC3A among Ferroptosis-Related Genes in Glioblastoma.

Glioblastoma (GBM) remains a highly aggressive malignancy, and the contribution of ferroptosis-related genes to disease susceptibility remains incompletely understood. A genetically anchored, multistage framework was applied to prioritize ferroptosis-related genes associated with GBM. Among 483 genes curated from FerrDb V2, 315 had candidate cis-expression quantitative trait loci (cis-eQTLs) in eQTLGen, 250 retained at least three independent instruments after linkage disequilibrium clumping, and 226 yielded valid inverse-variance weighted (IVW) Mendelian randomization estimates using a GBM genome-wide association study comprising 6,183 cases and 18,169 controls. Thirty-four genes met the exploratory discovery criteria of P < 0.05 and a Benjamini-Hochberg false discovery rate (BH-FDR) < 0.20, with directionally concordant Bayesian weighted Mendelian randomization (BWMR) estimates. Replication-stage Mendelian randomization using GTEx V10 whole-blood cis-eQTLs supported four genes: ATG7, RPTOR, MAP1LC3A, and CHMP6. Evaluation across three independent tumor-control transcriptomic cohorts demonstrated that MAP1LC3A was consistently downregulated in tumor tissue and showed a significant random-effects pooled estimate (log₂ fold change, -1.273; 95% confidence interval, -1.625 to -0.920; false discovery rate = 0.016), whereas the other three genes lacked comparable cross-cohort statistical support. Single-cell virtual knockout analysis was subsequently performed in a patient-balanced subset of 2,400 malignant cells selected from 4,916 eligible cells across 20 adult IDH-wild-type GBM tumors. Across five independently seeded runs, 3, 15, 4, and 7 robust downstream genes were identified for ATG7, RPTOR, MAP1LC3A, and CHMP6, respectively. The resulting consensus sets comprised 17 unique genes, with RND3 shared across all four targets. Gene Ontology analysis indicated enrichment of cell-adhesion and cell-surface processes, whereas no KEGG or Reactome pathways remained significant after multiple-testing correction. Collectively, these findings prioritize MAP1LC3A for future experimental investigation while distinguishing genetic association, tumor-expression concordance, and computational perturbation from definitive evidence of causality or mechanism.

Humans↗

PubMed2026

Distinct periarticular muscle transcriptomes: inflammation in rheumatoid arthritis versus metabolic dysregulation in osteoarthritis.

OBJECTIVES: Periarticular skeletal muscle abnormalities are recognised in rheumatoid arthritis (RA) and osteoarthritis (OA), but their divergent molecular pathologies are poorly defined. This study aimed to elucidate and directly compare the transcriptomic profiles of periarticular muscle in patients with RA and OA. METHODS: We performed bulk RNA sequencing of periarticular skeletal muscle samples collected during total joint arthroplasty from RA (n=6) and OA (n=4) patients. Differential gene expression analysis, weighted gene co-expression network analysis (WGCNA), pathway enrichment, and gene set variation analyses were conducted to identify disease-specific molecular features and their clinical associations. RESULTS: The two conditions showed fundamentally distinct profiles. RA muscle exhibited a pronounced inflammatory signature, characterised by upregulation of cytokine-responsive genes including FOS, EGR1, and CXCL2, and enrichment of tumour necrosis factor-α and interleukin-6 (IL-6)/JAK-STAT3 signalling. In contrast, OA muscle was characterised by metabolic dysregulation, with upregulation of genes linked to adipogenesis (PCK1, SFRP4) and significant enrichment of epithelial-to-mesenchymal transition (EMT) signalling. These divergent profiles were further supported by WGCNA, which identified distinct modules reflecting heightened innate immune and complement activation in RA, and disrupted metabolic processes in OA. Notably, in RA, the IL-2-STAT5 signalling pathway was unique among those tested in showing a strong positive correlation with DAS28-ESR (r=0.94, p=0.019). CONCLUSIONS: This study reveals distinct molecular pathologies in the periarticular muscle of RA and OA. RA muscle shows an intense inflammatory profile potentially linked to cachexia, whereas OA muscle displays features of metabolic disease and pro-fibrotic remodelling.

Humans↗

PubMed2004

The transcriptome: malariologists ride the wave.

The Plasmodium falciparum genome-sequencing project has provided malariologists with vast amounts of new information pertinent to a multitude of cellular processes that previously were only guessed about. In exploring this morass of predicted genes and proteins, there is now a danger of simply re-inventing the cell. Fortunately, new global transcriptional analyses reassure malariologists that they are not dealing with just "any old cell." The informative papers on the plasmodial transcriptome by Le Roch et al. (2003)1 and Bozdech et al. (2003)2 discussed below forge a bridge between the genomics and proteomics of P. falciparum. They are likely to act as a fulcrum upon which much future research will turn: for example, the study of regulation and feed-back loops.

Animals↗

PubMed2003

Combined transcriptome and proteome analysis of Escherichia coli during high cell density culture.

Combined transcriptome and proteome analysis was carried out to understand metabolic and physiological changes of Escherichia coli during the high cell density cultivation (HCDC). The expression of genes of TCA cycle enzymes, NADH dehydrogenase and ATPase, was up-regulated during the exponential fed-batch period and was down-regulated afterward. However, expression of most of the genes involved in glycolysis and pentose phosphate pathway was up-regulated at the stationary phase. The expression of most of amino acid biosynthesis genes was down-regulated as cell density increased, which seems to be the major reason for the reduced specific productivity of recombinant proteins during HCDC. The expression of chaperone genes increased with cell density, suggesting that the high cell density condition itself can be stressful to the cells. Severe competition for oxygen at high cell density seemed to make cells use cytochrome bd, which is less efficient but has a high oxygen affinity than cytochrome bo(3). Population cell density itself strongly affected the expression of porin protein genes, especially ompF, and hence the permeability of the outer membrane. Expression of phosphate starvation genes was most strongly up-regulated toward the end of cultivation. It was also found that sigma(E) (rpoE) plays a more important role than sigma(S) (rpoS) at the stationary phase of HCDC. These findings should be invaluable in designing metabolic engineering and fermentation strategies for the production of recombinant proteins and metabolites by HCDC of E. coli.

Amino Acid Sequence↗

PubMed2004

Metabolic pathway analysis of yeast strengthens the bridge between transcriptomics and metabolic networks.

Central carbon metabolism of the yeast Saccharomyces cerevisiae was analyzed using metabolic pathway analysis tools. Elementary flux modes for three substrates (glucose, galactose, and ethanol) were determined using the catabolic reactions occurring in yeast. Resultant elementary modes were used for gene deletion phenotype analysis and for the analysis of robustness of the central metabolism and network functionality. Control-effective fluxes, determined by calculating the efficiency of each mode, were used for the prediction of transcript ratios of metabolic genes in different growth media (glucose-ethanol and galactose-ethanol). A high correlation was obtained between the theoretical and experimental expression levels of 38 genes when ethanol and glucose media were considered. Such analysis was shown to be a bridge between transcriptomics and fluxomics. Control-effective flux distribution was found to be promising in in silico predictions by incorporating functionality and regulation into the metabolic network structure.

Biomass↗

PubMed2005

Transcriptome analysis of the murine forelimb and hindlimb autopod.

To gain insight into the coordination of gene expression profiles during forelimb and hindlimb differentiation, a transcriptome analysis of mouse embryonic autopod tissues was performed using Affymetrix Murine Gene Chips (MOE-430). Forty-four transcripts with expression differences higher than 2-fold (T test, P < or = 0.05) were detected between forelimb and hindlimb tissues including 38 new transcripts such as Rdh10, Frzb, Tbx18, and Hip that exhibit differential limb expression. A comparison of gene expression profiles in the forelimb, hindlimb, and brain revealed 24 limb-signature genes whose expression was significantly enriched in limb autopod versus brain tissue (fold change >2, P < or = 0.05). Interestingly, the genes exhibiting enrichment in the developing autopod also segregated into significant fore- and hindlimb-specific clusters (P < or = 0.05) suggesting that by E 12.5, unique gene combinations are being used during the differentiation of each autopod type.

Animals↗

PubMed2026

Phenotypic and transcriptomic characterization of biallelic RNU2-2 developmental and epileptic encephalopathy.

OBJECTIVE: A significant proportion of individuals with suspected genetic developmental and epileptic encephalopathies (DEEs) remain unsolved following whole genome sequencing (WGS). Here we describe biallelic RNU2-2 variants causing a recently reported, severe, recessive DEE. METHODS: We screened individuals who have received WGS analyses at the Genomic Medicine Centre Karolinska for Rare Diseases for biallelic RNU2-2 variants. Deep phenotyping was performed through reviewing entire medical histories and phenotypic traits were transcribed to their corresponding Human Phenotype Ontology (HPO) term. HPO terms were used to generate pairwise phenotypic similarity scores and assess for significantly shared phenotype enrichment in the RNU2-2 sub-cohort. RNA sequencing analyses were performed in fibroblast and blood tissues to compare splicing events between RNU2-2 individuals and two independent control groups. RESULTS: We identified 14 individuals from nine families with 12 ultra-rare biallelic RNU2-2 variants clustering in the conserved 5' domains. Genotype data from 13 of 14 individuals has been reported previously as part of a larger cohort. All individuals presented with a highly concordant, severe DEE, characterized by severe to profound intellectual disability, inability to walk or communicate, hyperkinesia, and refractory seizures. Infantile spasms and tonic seizures were the predominant seizure types and a Lennox-Gastaut syndrome-like phenotype was common. These individuals had a significantly similar phenotypic signature when compared with 703 individuals with complex pediatric epilepsies (two-sided Monte Carlo permutation test, p = .005). RNA sequencing analyses showed aberrant splicing, with the most pronounced effects in fibroblast tissues in mutually exclusive exon and alternate 3' splice-site events, which were not detectable in blood. SIGNIFICANCE: We present deep phenotyping data and transcriptomic analyses that provide support for rare, 5' clustering biallelic RNU2-2 variants causing this novel, severe DEE. We propose an RNA sequencing methodology on fibroblast tissue for future validation of RNU2-2 variants.

autosomal recessive disease↗