Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods,
together with associated computational tools and the growing availability of public data …

Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but
does not capture their spatial distribution nor reveal local networks of intercellular …

Map** single-cell sequencing profiles to comprehensive reference datasets provides a
powerful alternative to unsupervised analysis. However, most reference datasets are …

Approximately 60% of patients with large B cell lymphoma treated with chimeric antigen
receptor (CAR) T cell therapies targeting CD19 experience disease progression, and …

Understanding intratumoral heterogeneity—the molecular variation among cells within a
tumor—promises to address outstanding questions in cancer biology and improve the …

Chronic antigen stimulation during viral infections and cancer can lead to T cell exhaustion,
which is characterized by reduced effector function and proliferation, and the expression of …

Unmasking the subtleties of the immune system requires both a comprehensive knowledge
base and the ability to interrogate that system with intimate sensitivity. That task, to a …

Glioblastomas are aggressive primary brain cancers that recur as therapy-resistant tumors.
Myeloid cells control glioblastoma malignancy, but their dynamics during disease …

The simultaneous measurement of multiple modalities represents an exciting frontier for
single-cell genomics and necessitates computational methods that can define cellular states …

We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial
omics data, including various cell states, experimental designs and feature modalities, by …