Corinn Small

Hundreds of millions of single cells have been analyzed using high-throughput transcriptomic methods. The cumulative knowledge within these datasets provides an exciting opportunity for unlocking insights into health and disease at the level of single cells. Meta-analyses that span diverse datasets building on recent advances in large language models and other machine-learning approaches pose exciting new directions to model and extract insight from single-cell data. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, the sheer number of datasets, data models and accessibility remains a challenge. Here, we present CZ CELLxGENE Discover (cellxgene.cziscience.com), a data platform that provides curated and interoperable single-cell data. Available via a free-to-use online data portal, CZ CELLxGENE hosts a growing corpus of community-contributed data of over 93 million unique cells. Curated, standardized and associated with consistent cell-level metadata, this collection of single-cell transcriptomic data is the largest of its kind and growing rapidly via community contributions. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to explore individual datasets, perform cross-corpus analysis, and run meta-analyses of tens of millions of cells across studies and tissues at the resolution of single cells.

Abstract Hundreds of millions of single cells have been analyzed to date using high throughput transcriptomic methods, thanks to technological advances driving the increasingly rapid generation of single-cell data. This provides an exciting opportunity for unlocking new insights into health and disease, made possible by meta-analysis that span diverse datasets building on recent advances in large language models and other machine learning approaches. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, a major challenge remains the sheer number of datasets and inconsistent format, data models and accessibility. Many datasets are available via unique portals platforms that often lack interoperability. Here, we present CZ CellxGene Discover ( cellxgene.cziscience.com ), a data platform that provides curated and interoperable data. This single-cell data resource, available via a free-to-use online data portal, hosts a growing corpus of community contributed data that spans more than 50 million unique cells. Curated, standardized, and associated with consistent cell-level metadata, this collection of interoperable single-cell transcriptomic data is the largest of its kind. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to rapidly explore individual datasets and perform cross-corpus analysis. This functionality is enabling meta-analyses of tens of millions of cells across studies and tissues and providing global views of human cells at the resolution of single cells.

Spanning two decades, the collaborative ENCODE project aims to identify all the functional elements within human and mouse genomes. To best serve the scientific community, the comprehensive ENCODE data including results from 23,000+ functional genomics experiments, 800+ functional elements characterization experiments and 60,000+ results from integrative computational analyses are available on an open-access data-portal ( https://www.encodeproject.org/ ). The final phase of the project includes data from several novel assays aimed at characterization and validation of genomic elements. In addition to developing and maintaining the data portal, the Data Coordination Center (DCC) implemented and utilised uniform processing pipelines to generate uniformly processed data. Here we report recent updates to the data portal including a redesigned home page, an improved search interface, new custom-designed pages highlighting biologically related datasets and an enhanced cart interface for data visualisation plus user-friendly data download options. A summary of data generated using uniform processing pipelines is also provided.

CONTEXT.—: Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare hematologic malignancy with poor outcome. BPDCN diagnostically overlaps with entities such as acute myeloid leukemia, histiocytic/dendritic cell neoplasms, and natural killer/T-cell lymphomas. Unfortunately, large, patient-centered studies that comprehensively analyze clinical, pathologic, and other diagnostic features are lacking. As such, there is an incomplete understanding of this disease. OBJECTIVE.—: To better characterize BPDCN, a multicenter working group consisting of hematopathologists and dermatopathologists gathered in person and remotely to review the current understanding of BPDCN, discuss specific issues regarding the diagnosis and differential diagnosis, and perform a retrospective analysis of the literature. DATA SOURCES.—: The working group curated a database of published BPDCN patient cases (BPDCN Network literature database), and following careful discussion and review, 361 articles were identified, comprising a total of 1513 individually annotated patients. CONCLUSIONS.—: By conducting an in-depth analysis, not only did we confirm known findings such as frequent skin involvement (84% of patients; 861 of 1028) and a male predominance among older patients (>60 years old; male to female ratio of 3.5:1; 617:177), but we also identified a number of underrecognized features, such as significant central nervous system involvement (38% of cases; 24 of 64), and a more equal male to female prevalence among patients younger than 40 years (male to female ratio of 1.25:1; 167:134). Furthermore, we were able to accurately summarize the immunophenotypic, cytogenetic, and molecular features of this disease. BPDCN is a complex disease with distinct morphologic, immunophenotypic, and molecular findings. Continual updates of the literature database generated here and further analysis can allow for prospective refinement of our understanding of this orphan disease.

The wild relatives of modern tomato crops are native to South America. These plants occur in habitats as different as the Andes and the Atacama Desert and are, to some degree, all susceptible to fungal pathogens of the genus Alternaria. Alternaria is a large genus. On tomatoes, several species cause early blight, leaf spots and other diseases. We collected Alternaria-like infection lesions from the leaves of eight wild tomato species from Chile and Peru. Using molecular barcoding markers, we characterized the pathogens. The infection lesions were caused predominantly by small-spored species of Alternaria of the section Alternaria, like A. alternata, but also by Stemphylium spp., Alternaria spp. from the section Ulocladioides and other related species. Morphological observations and an infection assay confirmed this. Comparative genetic diversity analyses show a larger diversity in this wild system than in studies of cultivated Solanum species. As A. alternata has been reported to be an increasing problem in cultivated tomatoes, investigating the evolutionary potential of this pathogen is not only interesting to scientists studying wild plant pathosystems. It could also inform crop protection and breeding programs to be aware of potential epidemics caused by species still confined to South America.