Biology subjects

Dorman, L.

Publications and source records attributed to Dorman, L..

3 recordsLinked to original sources

bioRxiv2023

CZ CELLxGENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data

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.

cell biology↗

bioRxiv2023

Single-nucleus RNA sequencing provides insights into the GL261-GSC syngeneic mouse model of glioblastoma

Glioblastoma (GBM) is an aggressive tumor with very bad prognosis. The urgent need to find new effective therapies is challenged by the unique characteristics of GBM, including high intra and intertumoral heterogeneity. Using single-nucleus transcriptomics (snRNA-seq), we characterized the panorama of a preclinical immunocompetent murine model based in the implantation of mouse glioblastoma stem cells (GL261-GSCs) into the brain parenchyma. Additionally, we performed Visium spatial transcriptomics in the in vivo model to confirm the location of annotated cells. To understand the technical bias of this approach, we performed two scRNA-seq methods in GBM cells. We thoroughly characterized the tumor microenvironment (TME) at early and late stages of tumor development and upon treatment with temozolomide (TMZ), the standard of care for patients with GBM, and with Tat-Cx43266-283, a promising experimental treatment. We identified prominent GBM targets that can be addressed using this preclinical model, such as Grik2, Nlgn3, Gap43 or Kcnn4, which are involved in electrical and synaptic integration of GBM cells into neural circuits, as well as the expression of Nt5e, Cd274 or Irf8, which indicates the development of immune evasive properties in these GBM cells. In agreement, snRNA-seq unveiled high expression of several immunosuppressive-associated molecules in immune cells, such as Csf1r, Arg1, Mrc1 and Tgfb1, suggesting the development of an immunosuppressive microenvironment. We also show the landscape of cytokines, cytokine receptors, checkpoint ligands and receptors in tumor and TME cells, which are crucial data for a rational design of immunotherapy studies. Thus, Mrc1, PD-L1, TIM-3 or B7-H3 are among the immunotherapy targets that can be addressed in this model. Finally, the comparison of the preclinical GL261-GSC GBM model with human GBM subtypes unveiled important similarities with the recently identified TMEmed human GBM, indicating that preclinical data obtained in GL261-GSC GBM model might be applied to TMEmed human GBM, improving patient stratification in clinical trials. In conclusion, this work provides crucial information for future preclinical studies in GBM improving their clinical application.

neuroscience↗

bioRxiv2023

Tutorial: guidelines for manual cell type annotation of single-cell multi-omics datasets using interactive software

Assigning cell identity to clusters of single cells is an essential step towards extracting biological insights from many genomics datasets. Although annotation workflows for datasets built with a single modality are well established, limitations exist in annotating cell types in datasets with multiple modalities due to the need for a framework to exploit them jointly. While, in principle, different modalities could convey complementary information about cell identity, it is unclear to what extent they can be combined to improve the accuracy and resolution of cell type annotations. Here, we present a conceptual framework to examine and jointly interrogate distinct modalities to identify cell types. We integrated our framework into a series of vignettes, using immune cells as a well-studied example, and demonstrate cell type annotation workflows ranging from using single-cell RNA-seq datasets alone, to using multiple modalities such as single-cell Multiome (RNA and chromatin accessibility), CITE-seq (RNA and surface proteins). In some cases, one or other single modality is superior to the other for identification of specific cell types, in others combining the two modalities improves resolution and the ability to identify finer subpopulations. Finally, we use interactive software from CZ CELLxGENE community tools to visualize and integrate histological and spatial transcriptomic data.

bioinformatics↗