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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. Wu and colleagues develop a barcoding tool, ClonMapper, which permits single-cell lineage tracing and clonal isolation and demonstrate its utility to study clonal dynamics in human CLL cells in the context of chemotherapy treatment and resistance.

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.

SummaryA 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 data types. 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 mechanistic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal population-size data. We demonstrate that the explicit inclusion of the transcriptomic information in the parameter estimation is critical for identification of the model parameters and enables accurate prediction of new treatment regimens. Inclusion of the transcriptomic data improves predictive accuracy in new treatment response dynamics with a concordance correlation coefficient (CCC) of 0.89 compared to a prediction accuracy of CCC = 0.79 without integration of the single cell RNA sequencing (scRNA-seq) data directly into the model calibration. To the best our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with longitudinal treatment response data into a mechanistic mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multimodal data sets into identifiable mathematical models to develop optimized treatment regimens from data.

The T cell repertoire in each individual includes T cell receptors (TCRs) of enormous sequence diversity through the pairing of diverse TCR α- and β-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–β 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 TCRαβ 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 β-chain sequences were common in all individuals. In contrast, sharing of entirely identical TCRαβ 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 TCRαβ 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–β pairs with entire sequence identities.

The remarkable evolutionary capacity of cancer is a major challenge to current therapeutic efforts. Fueling this evolution is its vast clonal heterogeneity and ability to adapt to diverse selective pressures. Although the genetic and transcriptional mechanisms underlying these responses have been independently evaluated, the ability to couple genetic alterations present within individual clones to their respective transcriptional or functional outputs has been lacking in the field. To this end, we developed a high-complexity expressed barcode library that integrates DNA barcoding with single-cell RNA sequencing through use of the CROP-seq sgRNA expression/capture system, and which is compatible with the COLBERT clonal isolation workflow for subsequent genomic and epigenomic characterization of specific clones of interest. We applied this approach to study chronic lymphocytic leukemia (CLL), a mature B cell malignancy notable for its genetic and transcriptomic heterogeneity and variable disease course. Here, we demonstrate the clonal composition and gene expression states of HG3, a CLL cell line harboring the common alteration del(13q), in response to front-line cytotoxic therapy of fludarabine and mafosfamide (an analog of the clinically used cyclophosphamide). Analysis of clonal abundance and clonally-resolved single-cell RNA sequencing revealed that only a small fraction of clones consistently survived therapy. These rare highly drug tolerant clones comprise 94% of the post-treatment population and share a stable, pre-existing gene expression state characterized by upregulation of CXCR4 and WNT signaling and a number of DNA damage and cell survival genes. Taken together, these data demonstrate at unprecedented resolution the diverse clonal characteristics and therapeutic responses of a heterogeneous cancer cell population. Further, this approach provides a template for the high-resolution study of thousands of clones and the respective gene expression states underlying their response to therapy.

The common bed bug (Cimex lectularius) has been a persistent pest of humans for thousands of years, yet the genetic basis of the bed bug's basic biology and adaptation to dense human environments is largely unknown. Here we report the assembly, annotation and phylogenetic mapping of the 697.9-Mb Cimex lectularius genome, with an N50 of 971 kb, using both long and short read technologies. A RNA-seq time course across all five developmental stages and male and female adults generated 36,985 coding and noncoding gene models. The most pronounced change in gene expression during the life cycle occurs after feeding on human blood and included genes from the Wolbachia endosymbiont, which shows a simultaneous and coordinated host/commensal response to haematophagous activity. These data provide a rich genetic resource for mapping activity and density of C. lectularius across human hosts and cities, which can help track, manage and control bed bug infestations., The common bedbug is a pest for humans, yet its molecular biology is poorly understood. Here, the authors sequence the common bedbug genome and profile gene expression across all life stages to show major changes in gene expression after feeding on human blood.

Cancer's ability to evolve and adapt is a major challenge to therapeutic success. Fueling this evolution is vast tumor heterogeneity, with constituent clones varying in their genetics, epigenetics and response to therapy. As a field, however, we have yet to couple the genetic alterations present within individual clones to their transcriptional or functional outputs. Here, we applied a novel adaptation of clone tracing that integrates DNA barcoding with single-cell RNA sequencing (scRNA-seq) to HG3, a CLL cell line harboring del(13q) and no other known cancer drivers, to model in vitro responses to front-line chemotherapy with fludarabine and cyclophosphamide at clone-level resolution. To generate a high-diversity barcode library compatible with scRNA-seq, a random pool of 20 base pair DNA barcodes was introduced into the 3'UTR of a reporter gene in a lentiviral expression vector. This viral barcode library was transduced into HG3 cells (at MOI 0.1 to minimize multiple barcode-tagging of cells) and 1.2x106 barcoded cells were sorted and expanded to establish the parental barcoded HG3 population. We subsequently treated this barcoded population with an LD95 combined dose of fludarabine and mafosfamide (the in vitro analog of cyclophosphamide) in 8 parallel experiments. Cell barcodes were sequenced prior to treatment (TP1) and following outgrowth from treatment (TP2) for analysis of clonal composition. 10,000 cells each from TP1 and from 2 of 8 parallel replicates at TP2 were processed for scRNA-seq. We observed a massive decrease in viability across all 8 replicates, with regrowth occurring at 20 days post-treatment. Barcode analysis revealed a marked decrease in clonal diversity from TP1 to TP2 (11,827 to 2,622 ± 380, n=8; or ~78%), and clones that survived treatment did so consistently such that 94% of surviving cells in each replicate had a clonal identity that was present in all 8 replicates. Analysis of clonally-resolved transcriptional profiles revealed that clones consistently fell into one of two stable gene expression states (clusters) prior to treatment, with nominal intermixing between populations. Treatment predominantly selected for clones comprising the smaller of these two clusters (TP1-'high tolerance'), with only a minimal number of resistant clones originating from the larger cluster at TP1 (TP1-'low tolerance'). Pathway and gene set enrichment analysis of these two TP1 clusters demonstrated that TP1-high tolerance had a stark upregulation of common CLL signaling pathways (i.e. WNT and CXCR4, an inflammatory/migratory phenotype) and a reliance on chromatin modification pathways. TP1-low tolerance, on the other hand, exhibited upregulated type 2 antigen presentation and prostaglandin biosynthesis/metabolism which has a known role in driving inflammation and migration in adjacent cells (Wang et al, BMJ 2006). These gene expression states remained stable after treatment, but with the added upregulation of well-described mechanisms of resistance to cyclophosphamide, with TP1-high tolerance exhibiting upregulation of GSTP1, a glutathione S-transferase that is a main mediator of cyclophosphamide metabolism, and TP1-low tolerance exhibiting upregulation of members of the ALDH family thought to ameliorate toxicity from chemotherapy-induced ROS (Andersson et al, Acta Oncologica 1995). Through this work, we resolved the underlying clonal composition of a CLL cell line and observed that the constituent clones exhibit stable and discrete gene expression states that differentially respond to chemotherapy. We noted two different axes of resistance - a more successful avenue that relies on WNT and CXCR4 signaling as well as cyclophosphamide clearance for resistance, and a less-successful avenue that involves clearance of reactive oxygen species for survival. The intersection of these two critical CLL pathways in in vitro resistance to first-line CLL therapy is of particular interest given the FAT1 (WNT regulator) mutations and CXCR4 upregulation frequently seen in chemo-refractory CLL (Messina et al, Blood 2014; Burger et al, Blood 2006), and future efforts will assess the stability and interplay of these two pathways in patient samples collected upon relapse to fludarabine and cyclophosphamide. Further, our approach provides a template for the high-resolution study of tens of thousands of clones and their respective phenotypes in a mixed leukemic population. Disclosures Neuberg: Pharmacyclics: Research Funding; Madrigal Pharmaceuticals: Equity Ownership; Celgene: Research Funding. Wu:Pharmacyclics: Research Funding; Neon Therapeutics: Other: Member, Advisory Board.

Advances in next-generation sequencing have allowed insights into the enormous diversity of the human T cell receptor (TCR). Earlier studies have suggested that ~108 unique TRB genes exist at the periphery. In contrast, the factors that determine the pairing of TCRα and TCRβ chains have remained elusive due to the lack of high-throughput single-cell sequencing technology. We have developed a high-throughput single-cell sequencing technology that enables identification of >3×104 paired TRA:TRB sequences per sample and have conducted sequencing of naïve and memory CD4+ and of total T cells from 15 healthy individuals, including six pairs of monozygotic twins. We found that the frequencies of TRAV:TRBV pairing are essentially determined by the frequencies of the individual gene segments, suggesting the absence of structural constraints in the pairing of the α and β chains. Twins are more similar in the frequencies of TRAV and TRBV combinations than are unrelated individuals, but this difference was explained by a genetic influence on AV and BV gene segment frequencies. Twins and unrelated individuals were compared for the sharing of identical TCR sequences. TRA sequence sharing among twins was comparable to that among unrelated individuals, but TRB sharing was significantly higher in twins. Sharing of the entire TCR was regularly found in twins but not detected for naïve CD4+ T cells from unrelated individuals. Results were similar for naïve and memory T cells, with the genetic influence more prominent in memory T cells, suggesting the additional influence of peripheral selection. In support of this interpretation, the sharing of identical TCR sequences in unrelated individuals correlated with the number of shared MHC class II alleles.

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.

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.

Bacillus subtilis PS216, a strain isolated in Slovenia, has been sequenced. PS216 is transformable and forms robust biofilms, making it useful for the study of competence regulation in an undomesticated bacterium.