Vaeda computationally annotates doublets in single-cell RNA sequencing data.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) continues to expand our knowledge by facilitating the study of transcriptional heterogeneity at the level of single cells. Despite this technology's utility and success in biomedical research, technical artifacts are present in scRNA-seq data. Doublets/multiplets are a type of artifact that occurs when two or more cells are tagged by the same barcode, and therefore they appear as a single cell. Because this introduces non-existent transcriptional profiles, doublets can bias and mislead downstream analysis. To address this limitation, computational methods to annotate and remove doublets form scRNA-seq datasets are needed. RESULTS: We introduce vaeda (Variational Auto-Encoder for Doublet Annotation), a new approach for computational annotation of doublets in scRNA-seq data. Vaeda integrates a variational auto-encoder and Positive-Unlabeled learning to produce doublet scores and binary doublet calls. We apply vaeda, along with seven existing doublet annotation methods, to 16 benchmark datasets and find that vaeda performs competitively in terms of doublet scores and doublet calls. Notably, vaeda outperforms other python-based methods for doublet annotation. Altogether, vaeda is a robust and competitive method for scRNA-seq doublet annotation and may be of particular interest in the context of python-based workflows. AVAILABILITY AND IMPLEMENTATION: Vaeda is available at https://github.com/kostkalab/vaeda, and the version used for the results we present here is archived at zenodo (https://doi.org/10.5281/zenodo.7199783). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computational profiling of hiPSC-derived heart organoids reveals chamber defects associated with NKX2-5 deficiency.

Heart organoids have the potential to generate primary heart-like anatomical structures and hold great promise as in vitro models for cardiac disease. However, their properties have not yet been fully studied, which hinders their wide spread application. Here we report the development of differentiation systems for ventricular and atrial heart organoids, enabling the study of heart diseases with chamber defects. We show that our systems generate chamber-specific organoids comprising of the major cardiac cell types, and we use single cell RNA sequencing together with sample multiplexing to characterize the cells we generate. To that end, we developed a machine learning label transfer approach leveraging cell type, chamber, and laterality annotations available for primary human fetal heart cells. We then used this model to analyze organoid cells from an isogeneic line carrying an Ebstein's anomaly associated genetic variant in NKX2-5, and we successfully recapitulated the disease's atrialized ventricular defects. In summary, we have established a workflow integrating heart organoids and computational analysis to model heart development in normal and disease states.

Global and age-related neuroanatomical abnormalities in a Pax6-deficient mouse model of aniridia suggests a role for Pax6 in adult structural neuroplasticity.

PAX6 encodes a highly conserved transcription factor necessary for normal development of the eyes and central nervous system. Heterozygous loss-of-function mutations in PAX6 cause the disorder aniridia in humans and the Small eye trait in mice. Aniridia is a congenital and progressive disorder known for ocular phenotypes; however, recently, consequences of PAX6 haploinsufficiency in the brains of aniridia patients have been identified. These findings span structural and functional abnormalities, including deficits in cognitive and sensory processing. Furthermore, some of these abnormalities are accelerated as aniridia patients age. Although some functional abnormalities may be explained by structural changes, variability of results remain, and the effects of PAX6 heterozygous loss-of-function mutations on neuroanatomy, particularly with regard to aging, have yet to be resolved. Our study used high-resolution magnetic resonance imaging (MRI) and histology to investigate structural consequences of such mutations in the adult brain of our aniridia mouse model, Small eye Neuherberg allele (Pax6SeyNeu/+), at two adult age groups. Using both MRI and histology enables a direct comparison with human studies, while providing higher resolution for detection of more subtle changes. We show volumetric changes in major brain regions of the the Pax6SeyNeu/+ mouse compared to wild-type including genotype- and age-related olfactory bulb differences, age-related cerebellum differences, and genotype-related eye differences. We also show alterations in thickness of major interhemispheric commissures, particularly those anteriorly located within the brain including the optic chiasm, corpus callosum, and anterior commissure. Together, these genotype and age related changes to brain volumes and structures suggest a global decrease in adult brain structural plasticity in our Pax6SeyNeu/+ mice.