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Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata Specifications that extend the OME Data Model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.

Calcific aortic valve stenosis (CAVS) is a common and life-threatening heart disease and the current treatment options cannot stop or delay its progression. A GWAS on 1009 cases and 1017 ethnically matched controls was combined with a large-scale eQTL mapping study of human aortic valve tissues ( n  = 233) to identify susceptibility genes for CAVS. Replication was performed in the UK Biobank, including 1391 cases and 352,195 controls. A transcriptome-wide association study (TWAS) reveals PALMD (palmdelphin) as significantly associated with CAVS. The CAVS risk alleles and increasing disease severity are both associated with decreased mRNA expression levels of PALMD in valve tissues. The top variant identified shows a similar effect and strong association with CAVS ( P  = 1.53 × 10 −10 ) in UK Biobank. The identification of PALMD as a susceptibility gene for CAVS provides insights into the genetic nature of this disease, opens avenues to investigate its etiology and to develop much-needed therapeutic options. Progressive remodeling and calcification of the aortic valve leads to calcific aortic valve stenosis (CAVS) and, ultimately, heart failure. In a combined GWAS and TWAS approach, Thériault et al. identify PALMD as a candidate causal gene for CAVS, which is further supported by Mendelian randomization.

There is currently no medical therapy to prevent calcific aortic valve stenosis (CAVS). Multi-omics approaches could lead to the identification of novel molecular targets. Here, we perform a genome-wide association study (GWAS) meta-analysis including 14,819 cases among 941,863 participants of European ancestry. We report 32 genomic loci, among which 20 are novel. RNA sequencing of 500 human aortic valves highlights an enrichment in expression regulation at these loci and prioritizes candidate causal genes. Homozygous genotype for a risk variant near TWIST1 , a gene involved in endothelial-mesenchymal transition, has a profound impact on aortic valve transcriptomics. We identify five genes outside of GWAS loci by combining a transcriptome-wide association study, colocalization, and Mendelian randomization analyses. Using cross-phenotype and phenome-wide approaches, we highlight the role of circulating lipoproteins, blood pressure and inflammation in the disease process. Our findings pave the way for the development of novel therapies for CAVS. Here the authors report 20 novel genomic risk loci for calcific aortic valve stenosis, the most common heart valve disorder. Using RNA sequencing in 500 human aortic valves, they prioritize candidate causal genes including TWIST1 , a gene involved in endothelial-mesenchymal transition.

Pulmonary neuroendocrine neoplasms are classified by WHO as either typical or atypical carcinoids, large cell (LCNEC) or small cell (SCLC) neuroendocrine carcinoma based on mitotic count, morphology, and necrosis assessment. LCNEC with low mitotic count and sharing morphologic features with carcinoids are in a gray zone for classification and their rare prevalence and the paucity of studies precludes proper validation of the current grading system. In this study, we aim to investigate their clinicopathological and transcriptomic profiles. Lung resection specimens obtained from 18 patients diagnosed with carcinoids or LCNEC were selected. Four of them were characterized as borderline tumors based on a mitotic rate ranging between 10 and 30 mitoses per 2 mm2. Comprehensive morphological and immunohistochemical (IHC) evaluation was performed and tumor-based transcriptomic profiles were analyzed through unsupervised clustering. Clustering analysis revealed two distinct molecular groups characterized by low (C1) and high (C2) proliferation. C1 was comprised of seven carcinoids and three borderline tumors, while C2 was comprised of seven LCNEC and one borderline tumor. Furthermore, patients in cluster C1 had a better recurrence-free survival compared with patients in cluster C2 (20% vs 75%). Histological features, IHC profile, and molecular analysis showed that three out of four borderline tumors showed features consistent with carcinoids. Therefore, our findings convey that the current diagnostic guidelines are suboptimal for classification of pulmonary neuroendocrine tumors with increased proliferative index and carcinoid-like morphology. These results support the emerging concept that neuroendocrine tumors with carcinoid-like features and mitotic count of <20 mitoses per 2 mm2 should be regarded as pulmonary carcinoids instead of LCNEC.

To identify candidate causal genes of asthma, we performed a genome-wide association study (GWAS) in UK Biobank on a broad asthma definition (n = 56,167 asthma cases and 352,255 controls). We then carried out functional mapping through transcriptome-wide association studies (TWAS) and Mendelian randomization in lung (n = 1,038) and blood (n = 31,684) tissues. The GWAS reveals 72 asthma-associated loci from 116 independent significant variants (PGWAS < 5.0E-8). The most significant lung TWAS gene on 17q12-q21 is GSDMB (PTWAS = 1.42E-54). Other TWAS genes include TSLP on 5q22, RERE on 1p36, CLEC16A on 16p13, and IL4R on 16p12, which all replicated in GTEx lung (n = 515). We demonstrate that the largest fold enrichment of regulatory and functional annotations among asthma-associated variants is in the blood. We map 485 blood eQTL-regulated genes associated with asthma and 50 of them are causal by Mendelian randomization. Prioritization of druggable genes reveals known (IL4R, TSLP, IL6, TNFSF4) and potentially new therapeutic targets for asthma.Kim Valette et al. perform a genomic study on asthma integrating genome-wide association study, functional mapping using lung and blood transcriptome-wide profiles, as well as Mendelian randomization. They show candidate causal genes expressed in lung and blood tissues that are putative therapeutic targets for asthma.

We introduce a framework for end-to-end integrative modeling of 3D single-cell multi-channel fluorescent image data of diverse subcellular structures. We employ stacked conditional β -variational autoencoders to first learn a latent representation of cell morphology, and then learn a latent representation of subcellular structure localization which is conditioned on the learned cell morphology. Our model is flexible and can be trained on images of arbitrary subcellular structures and at varying degrees of sparsity and reconstruction fidelity. We train our full model on 3D cell image data and explore design trade-offs in the 2D setting. Once trained, our model can be used to predict plausible locations of structures in cells where these structures were not imaged. The trained model can also be used to quantify the variation in the location of subcellular structures by generating plausible instantiations of each structure in arbitrary cell geometries. We apply our trained model to a small drug perturbation screen to demonstrate its applicability to new data. We show how the latent representations of drugged cells differ from unperturbed cells as expected by on-target effects of the drugs.

Bicuspid aortic valve (BAV) is the most frequent congenital heart defect and has a male predominance of 3 to 1. A large proportion of patients develop valvular and aortic complications. Despite the high prevalence of BAV, its cause and genetic origins remain elusive. The goal of this study was to identify genetic variants associated with BAV. Nine genes previously associated with BAV (NOTCH1, AXIN1, EGFR, ENG, GATA5, NKX2-5, NOS3, PDIA2, and TGFBR2) were sequenced in 48 patients with BAV using the Ion Torrent Personal Genome Machine. Pathogenicity of genetic variants was evaluated with the Combined Annotation Dependent Depletion framework. A selection of 89 variants identified by sequencing or in previous BAV genetic studies was genotyped, and allele frequencies were compared in 323 patients with BAV confirmed at surgery and 584 controls. Analyses were also performed by gender. Nine novel and 19 potentially pathogenic variants were identified by next-generation sequencing and confirmed by Sanger sequencing, but they were not associated with BAV in the case-control population. A significant association was observed between an in silico–predicted benign EGFR intronic variant (rs17290301) and BAV. Analyses performed by gender revealed different variants associated with BAV in men (EGFR rs533525993 and TEX26 rs12857479) and women (NOTCH1 rs61751489, TGFBR2 rs1155705, and NKX2-5 rs2277923). In conclusion, these results constitute the first association between EGFR genetic variants and BAV in humans and support a possible role of gender-specific polymorphisms in the development of BAV.

Introduction: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related mortality worldwide. Acinar is the most prevalent architectural pattern and is associated with an intermediate prognosis. Several studies have investigated the prognosis of acinar-predominant LUAD patients. Here, we aimed to move beyond the acinar-predominant classification and gain a more comprehensive understanding of how acinar minor components influence prognosis specifically when accompanying other histological patterns in LUAD. Methods: Patients were grouped by the proportion of acinar patterns in their tumors: acinar-predominant (AP), and acinar component (AC; non-acinar predominant LUAD with an acinar component of ≥5%). The clinicopathologic characteristics, recurrence-free survival (RFS), and a panel of well-characterized driver mutations, including KRAS, EGFR, BRAF, MET, and PIK3CA, were investigated in the two groups of patients. Results: Among 1263 LUAD patients, 716 (56.7%) were AP, and 547 (43.3%) were AC. In AP, the frequency of EGFR exon 19 deletions (EGFR-Del 19) was significantly higher than in AC (p = 0.014). AC demonstrated a worse RFS than AP in the unadjusted analysis (log-rank p: 0.006). In stage I, the difference in the RFS of AC in comparison to AP remained significant (p = 0.048). In the multivariable analysis, AC was significantly associated with a worse RFS in comparison to AP (hazard ratio [HR] AC vs. AP: 1.240, 95% CI: 1.103–1.312, p: 0.04), even after adjusting for other histological patterns, the mutational status, and relevant clinicopathological features. The post-recurrence survival was significantly better in patients with an acinar component of ≥5% who received EGFR tyrosine kinase inhibitors (TKIs) compared to those who did not receive TKIs (p = 0.033). Conclusions: While the predominant pattern primarily dictates prognosis in LAUD, the presence of an acinar minor component alongside other high-grade patterns may further worsen outcomes. This underscores the necessity of considering the broader histological landscape rather than focusing solely on predominant patterns, as our findings show that minor acinar components can impact RFS alongside other histological patterns.

Visual cortex is organized into discrete sub-regions or areas that are arranged into a hierarchy and serves different functions in the processing of visual information. In retinotopic maps of mouse cortex, there appear to be substantial mouse-to-mouse differences in visual area location, size and shape. Here we quantify the biological variation in the size, shape and locations of 11 visual areas in the mouse, after separating biological variation and measurement noise. We find that there is biological variation in the locations and sizes of visual areas.