B cell responses result in clonal expansion, and can occur in a variety of tissues. To define how B cell clones are distributed in the body, we sequenced 933,427 B cell clonal lineages and mapped them to 8 different anatomic compartments in 6 human organ donors. We show that large B cell clones partition into two broad networks—one spans the blood, bone marrow, spleen and lung, while the other is restricted to tissues within the gastrointestinal (GI) tract (jejunum, ileum and colon). Notably, GI tract clones display extensive sharing of sequence variants among different portions of the tract and have higher frequencies of somatic hypermutation, suggesting extensive and serial rounds of clonal expansion and selection. Our findings provide an anatomic atlas of B cell clonal lineages, their properties and tissue connections. This resource serves as a foundation for studies of tissue-based immunity, including vaccine responses, infections, autoimmunity and cancer.

Type 1 Diabetes (T1D) is an autoimmune disease in which immune cells destroy insulin-producing beta cells. The etiology of this complex disease is dependent on the interplay of multiple heterogeneous cell types in the pancreatic environment. Here, we provide a single-cell atlas of pancreatic islets of 24 T1D, autoantibody-positive, and non-diabetic organ donors across multiple quantitative modalities including ~80,000 cells using single-cell transcriptomics, ~7,000,000 cells using cytometry by time-of-flight, and ~1,000,000 cells using in situ imaging mass cytometry. We develop an advanced integrative analytical strategy to assess pancreatic islets and identify canonical cell types. We show that a subset of exocrine ductal cells acquires a signature of tolerogenic dendritic cells in an apparent attempt at immune suppression in T1D donors. Our multimodal analyses delineate cell types and processes that may contribute to T1D immunopathogenesis and provide an integrative procedure for exploration and discovery of human pancreas function.

Identifying and visualizing transcriptionally similar cells is instrumental for accurate exploration of cellular diversity revealed by single-cell transcriptomics. However, widely used clustering and visualization algorithms produce a fixed number of cell clusters. A fixed clustering “resolution” hampers our ability to identify and visualize echelons of cell states. We developed TooManyCells, a suite of graph-based algorithms for efficient and unbiased identification and visualization of cell clades. TooManyCells introduces a novel visualization model built on a concept intentionally orthogonal to dimensionality reduction methods. TooManyCells is also equipped with an efficient matrix-free divisive hierarchical spectral clustering wholly different from prevalent single-resolution clustering methods. Together, TooManyCells enables multi-resolution and multifaceted exploration of single-cell clades. An advantage of this paradigm is the immediate detection of rare and common populations that outperforms popular clustering and visualization algorithms as demonstrated using existing single-cell transcriptomic data sets and new data modeling drug resistance acquisition in leukemic T cells.