BACKGROUND:Alcohol use disorder (AUD) is a prevalent neuropsychiatric disorder that is a major global health concern, affecting millions of people worldwide. Previous studies of AUD used underpowered single-cell analysis or bulk homogenates of postmortem brain tissue, which obscure gene expression changes in specific cell types. Therefore, we sought to conduct the largest-to-date single-nucleus RNA sequencing (snRNA-seq) postmortem brain study in AUD to elucidate transcriptomic pathology with cell type-specific resolution. METHODS:Here, we performed snRNA-seq and high-dimensional network analysis of 73 postmortem samples from individuals with AUD (n = 36, nnuclei = 248,873) and neurotypical control individuals (n = 37, nnuclei = 210,573) in the dorsolateral prefrontal cortex from both male and female donors. Additionally, we performed analysis for cell type-specific enrichment of aggregate genetic risk for AUD as well as integration of the AUD proteome for secondary validation. RESULTS:We identified 32 distinct cell clusters and found widespread cell type-specific transcriptomic changes across the cortex in AUD, particularly affecting glial populations. We found the greatest dysregulation in novel microglial and astrocytic subtypes that accounted for the majority of differential gene expression and coexpression modules linked to AUD. Differential gene expression was secondarily validated by integration of a publicly available AUD proteome. Finally, analysis for aggregate genetic risk for AUD identified subtypes of glia as potential key players not only affected by but also causally linked to the progression of AUD. CONCLUSIONS:These results highlight the importance of cell type-specific molecular changes in AUD and offer opportunities to identify novel targets for treatment on the single-nucleus level.

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.

Background Alcoholism remains a prevalent health concern throughout the world. Previous studies have identified transcriptomic patterns in the brain associated with alcohol dependence in both humans and animal models. But none of these studies have systematically investigated expression within the unique cell types present in the brain. Results We utilized single nucleus RNA sequencing (snRNA-seq) to examine the transcriptomes of over 16,000 nuclei isolated from prefrontal cortex of alcoholic and control individuals. Each nucleus was assigned to one of seven major cell types by unsupervised clustering. Cell type enrichment patterns varied greatly among neuroinflammatory-related genes, which are known to play roles in alcohol dependence and neurodegeneration. Differential expression analysis identified cell type-specific genes with altered expression in alcoholics. The largest number of differentially expressed genes (DEGs), including both protein-coding and non-coding, were detected in astrocytes, oligodendrocytes, and microglia. Conclusions To our knowledge, this is the first single cell transcriptome analysis of alcohol-associated gene expression in any species, and the first such analysis in humans for any addictive substance. These findings greatly advance understanding of transcriptomic changes in the brain of alcohol-dependent individuals.

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.

Abstract The immense evolutionary capacity of cancer poses major challenges to current therapeutic efforts, and results from the vast clonal heterogeneity and ability of individual cancer cells to adapt to diverse selective pressures. More complete mechanistic understanding of the basis of therapeutic resistance has been limited by difficulties in coupling genetic identity of cells to their respective transcriptomic and functional outputs. To this end, we developed a novel high-complexity expressed barcode system, ClonMapper, that integrates DNA barcoding with single-cell RNA-sequencing and clonal isolation to characterize thousands of clones within a mixed cancer cellpopulation. In applying this system to breast cancer and B cell cancer cell lines in the setting of resistance to doxorubicin and fludarabine-based chemotherapy, we discover pretreatment sub-populations with distinct expression profiles that not only confer distinct treatment survivorship trajectories, but also provide the basis for long-term clonal equilibrium between co-existing clones in the absence of treatment. These data reveal the diverse clonal characteristics and therapeutic responses of a heterogeneous cancer cell population and highlight the unprecedented resolution that can be achieved using ClonMapper. Citation Format: Eric A. Brenner, Daylin Morgan, Aziz Al'Khafaji, Catherine Gutierrez, Catherine J. Wu, Amy Brock. High resolution analysis of clonal dynamics using lineage tracing and single cell transcriptomics [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-097.

Nongenetic heterogeneity in cancer plays a critical role in disease progression and response to therapy. While variability in cellular phenotypes results from both gene expression noise and different stable phenotypic states, in this chapter we will focus on theory and evidence for nongenetic heterogeneity due to phenotypic plasticity of cancer cells. To elucidate the theory that allows for heterogeneous populations of cells regardless of the genomic state, we incorporate the concept of the phenotypic landscape—in which cells reside in stable "attractor" states. Cells have the ability to transition to different states, and the probability of these transitions may be in part dependent on environmental conditions and independent of the genome. Mathematical models allow us to build a simplified understanding of heterogeneous states and the transition rates between states within a cancer cell population. Here we discuss ways that cell states are identified and measured to investigate nongenetic heterogeneity in various empirical settings. For example, we describe one of the most well-characterized cell state transitions, the epithelial-to-mesenchymal transition (EMT) observed in cancer cells in response to environmental cues. Because EMT is a physiological process in development and tissue repair, the accompanying cell state transitions are well defined and can be confirmed by multiple readouts. Additionally, we describe instances of nongenetic heterogeneity and phenotypic state switching defined by other cell states with functional relevance to cancer progression and drug response. To make progress in preventing the seemingly inexorable onset of chemoresistance, it is necessary to elucidate how drug exposure directly induces cell state transitions between sensitive and resistant cell states. We discuss here the evidence that exposure to cytotoxic or targeted therapeutic treatments may cause cells to activate transcriptional or cell signaling programs that render them insensitive to treatment via a variety of different resistance mechanisms.

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.

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.