Determinants governing T cell receptor α/ß-chain pairing in repertoire formation of identical twins.

The T cell repertoire in each individual includes T cell receptors (TCRs) of enormous sequence diversity through the pairing of diverse TCR α- and ß-chains, each generated by somatic recombination of paralogous gene segments. Whether the TCR repertoire contributes to susceptibility to infectious or autoimmune diseases in concert with disease-associated major histocompatibility complex (MHC) polymorphisms is unknown. Due to a lack in high-throughput technologies to sequence TCR α-ß pairs, current studies on whether the TCR repertoire is shaped by host genetics have so far relied only on single-chain analysis. Using a high-throughput single T cell sequencing technology, we obtained the largest paired TCRαß dataset so far, comprising 965,523 clonotypes from 15 healthy individuals including 6 monozygotic twin pairs. Public TCR α- and, to a lesser extent, TCR ß-chain sequences were common in all individuals. In contrast, sharing of entirely identical TCRαß amino acid sequences was very infrequent in unrelated individuals, but highly increased in twins, in particular in CD4 memory T cells. Based on nucleotide sequence identity, a subset of these shared clonotypes appeared to be the progeny of T cells that had been generated during fetal development and had persisted for more than 50 y. Additional shared TCRαß in twins were encoded by different nucleotide sequences, implying that genetic determinants impose structural constraints on thymic selection that favor the selection of TCR α-ß pairs with entire sequence identities.

Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer.

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other types of longitudinal data. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal bulk cell population (bulk time course) data. We demonstrate that the explicit inclusion of the phenotypic composition estimate, derived from single cell RNA-sequencing data (scRNA-seq), improves accuracy in the prediction of new treatments with a concordance correlation coefficient (CCC) of 0.92 compared to a prediction accuracy of CCC = 0.64 when fitting on longitudinal bulk cell population data alone. To our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with bulk time-course data to jointly calibrate a mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multiple data types into mathematical models to develop optimized treatment regimens from data.

Assembly and diploid architecture of an individual human genome via single-molecule technologies.

We present the first comprehensive analysis of a diploid human genome that combines single-molecule sequencing with single-molecule genome maps. Our hybrid assembly markedly improves upon the contiguity observed from traditional shotgun sequencing approaches, with scaffold N50 values approaching 30 Mb, and we identified complex structural variants (SVs) missed by other high-throughput approaches. Furthermore, by combining Illumina short-read data with long reads, we phased both single-nucleotide variants and SVs, generating haplotypes with over 99% consistency with previous trio-based studies. Our work shows that it is now possible to integrate single-molecule and high-throughput sequence data to generate de novo assembled genomes that approach reference quality.

Two methods for full-length RNA sequencing for low quantities of cells and single cells.

The ability to determine the gene expression pattern in low quantities of cells or single cells is important for resolving a variety of problems in many biological disciplines. A robust description of the expression signature of a single cell requires determination of the full-length sequence of the expressed mRNAs in the cell, yet existing methods have either 3' biased or variable transcript representation. Here, we report our protocols for the amplification and high-throughput sequencing of very small amounts of RNA for sequencing using procedures of either semirandom primed PCR or phi29 DNA polymerase-based DNA amplification, for the cDNA generated with oligo-dT and/or random oligonucleotide primers. Unlike existing methods, these protocols produce relatively uniformly distributed sequences covering the full length of almost all transcripts independent of their sizes, from 1,000 to 10 cells, and even with single cells. Both protocols produced satisfactory detection/coverage of the abundant mRNAs from a single K562 erythroleukemic cell or a single dorsal root ganglion neuron. The phi29-based method produces long products with less noise, uses an isothermal reaction, and is simple to practice. The semirandom primed PCR procedure is more sensitive and reproducible at low transcript levels or with low quantities of cells. These methods provide tools for mRNA sequencing or RNA sequencing when only low quantities of cells, a single cell, or even degraded RNA are available for profiling.

Multifunctional barcoding with ClonMapper enables high-resolution study of clonal dynamics during tumor evolution and treatment.

Lineage-tracing methods have enabled characterization of clonal dynamics in complex populations, but generally lack the ability to integrate genomic, epigenomic and transcriptomic measurements with live-cell manipulation of specific clones of interest. We developed a functionalized lineage-tracing system, ClonMapper, which integrates DNA barcoding with single-cell RNA sequencing and clonal isolation to comprehensively characterize thousands of clones within heterogeneous populations. Using ClonMapper, we identified subpopulations of a chronic lymphocytic leukemia cell line with distinct clonal compositions, transcriptional signatures and chemotherapy survivorship trajectories; patterns that were also observed in primary human chronic lymphocytic leukemia. The ability to retrieve specific clones before, during and after treatment enabled direct measurements of clonal diversification and durable subpopulation transcriptional signatures. ClonMapper is a powerful multifunctional approach to dissect the complex clonal dynamics of tumor progression and therapeutic response.

Plasma cell output from germinal centers is regulated by signals from Tfh and stromal cells.

Germinal centers (GCs) are the sites where B cells undergo affinity maturation. The regulation of cellular output from the GC is not well understood. Here, we show that from the earliest stages of the GC response, plasmablasts emerge at the GC-T zone interface (GTI). We define two main factors that regulate this process: Tfh-derived IL-21, which supports production of plasmablasts from the GC, and TNFSF13 (APRIL), which is produced by a population of podoplanin+ CD157high fibroblastic reticular cells located in the GTI that are also rich in message for IL-6 and chemokines CXCL12, CCL19, and CCL21. Plasmablasts in the GTI express the APRIL receptor TNFRSF13B (TACI), and blocking TACI interactions specifically reduces the numbers of plasmablasts appearing in the GTI. Plasma cells generated in the GTI may provide an early source of affinity-matured antibodies that may neutralize pathogens or provide feedback regulating GC B cell selection.