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.

Highlights d Oncogenic Notch repositions enhancers, but not contact domains and compartments d Notch-instructed and preformed loops control direct Notch target gene expression d Enhancer and promoter elements spatially converge into regulatory 3D cliques d Notch preferentially targets hyperconnected 3D cliques to regulate key oncogenes

In chronic infections, the immune response fails to control virus, leading to persistent antigen stimulation and the progressive development of T cell exhaustion. T cell effector differentiation is poorly understood in the context of exhaustion, but targeting effector programs may provide new strategies for reinvigorating T cell function. We identified Tribbles pseudokinase 1 (Trib1) as a central regulator of antiviral T cell immunity, where loss of Trib1 led to a sustained enrichment of effector-like KLRG1+ T cells, enhanced function, and improved viral control. Single-cell profiling revealed that Trib1 restrains a population of KLRG1+ effector CD8 T cells that is transcriptionally distinct from exhausted cells. Mechanistically, we identified an interaction between Trib1 and the T cell receptor (TCR) signaling activator, MALT1, which disrupted MALT1 signaling complexes. These data identify Trib1 as a negative regulator of TCR signaling and downstream function, and reveal a link between Trib1 and effector versus exhausted T cell differentiation that can be targeted to improve antiviral immunity.

The immune system can detect most invading pathogens. The potential for detection of pathogens is dependent on the somatic diversity of the immune repertoires. While it is known that this somatic diversity is carefully generated, it is unclear how the diversity is distributed in the different genes encoding receptors of immune cells. Utilizing different metrics for richness and diversity at the level of small sequence fragments, we present here an analysis of the entire known human germline repertoire as represented by the sequences from the ImMunoGeneTics database of immune receptors. We have developed a fragment sequence quantification analysis to track variation of repertoires with different degrees of precision. Somatic diversity has previously been functionally characterized mostly by division of the V gene sequences into the more conserved and invariant framework (FR) of the receptor and more varied complementarity determining regions (CDR), that interact with the antigen. We find that CDR and FR can be explicitly identified with our sequence fragment diversity quantification technique. In terms of diversity, CDR and FR are especially distinct in B cell V genes. T cell V genes show less of the CDR/FR periodicity but are more diverse overall. Our analysis further shows that there are other areas of diversity outside the CDR and FR that are found widely dispersed in T cell receptor V genes and more tightly focused in FR1 and FR3 in the B cell receptor V genes. The diversity we observe is not dependent on allelic differences nor is this diversity segregated by individual V gene families. We would thus expect that each individual exhibit a diversity equivalent to that of the entire potential repertoire.

Chromatin loops enable transcription factor-bound distal enhancers to interact with their target promoters to regulate transcriptional programs. Although developmental transcription factors, such as active forms of Notch, can directly stimulate transcription by activating enhancers, the effect of their oncogenic subversion on the 3-dimensional (3D) organization of the cancer genome is largely undetermined. By mapping chromatin looping genome-wide in Notch-dependent triplenegative breast cancer and B-cell lymphoma, we show that far beyond the well-characterized role of Notch as an activator of distal enhancers, Notch regulates its direct target genes through establishing new long-range regulatory interactions. Moreover, a large fraction of Notch-promoted regulatory loops forms highly interacting enhancer and promoter spatial clusters, termed "3D cliques". Loss-and gain-of-function experiments show that Notch preferentially targets hyperconnected 3D cliques that regulate the expression of crucial proto-oncogenes. Our observations suggest that oncogenic hijacking of developmental transcription factors can dysregulate transcription through widespread effects on the spatial organization of cancer genomes.

Recurrent internal tandem duplication (ITD) mutations are observed in various cancers including acute myeloid leukemia (AML), where ITD mutations in tyrosine kinase receptor FLT3 are associated with poor prognostic outcomes. Several FLT3 inhibitors (FLT3i) are in clinical trials for high-risk -ITD-positive AML. However, the variability of survival following FLT3i treatment suggests that the mere presence of-ITD mutations might not guarantee effective clinical response. Motivated by the heterogeneity of -ITD mutations, we investigated the effects of-ITD structural features on the response of AML patients to treatment. We developed the HeatITup (HEAT diffusion for Internal Tandem dUPlication) algorithm to identify and quantitate ITD structural features including nucleotide composition. Using HeatITup, we studied the impact of ITD structural features on the clinical response to FLT3i and induction chemotherapy in -ITD-positive AML patients. HeatITup accurately identifies and classifies ITDs into newly defined categories of "typical" or "atypical" based on their nucleotide composition. A typical ITD's insert sequence completely matches the wild-type , whereas an atypical ITD's insert contains nucleotides exogenous to the wild-type Our analysis shows marked divergence between typical and atypical ITD mutation features. Furthermore, our data suggest that AML patients carrying typical ITDs benefited significantly more from both FLT3i and induction chemotherapy treatments than patients with atypicalITDs. These results underscore the importance of structural discernment of complex somatic mutations such as ITDs in progressing toward personalized treatment of AML patients, and enable researchers and clinicians to unravel ITD complexity using the provided software.