Diverse clonal fates emerge upon drug treatment of homogeneous cancer cells.

Even among genetically identical cancer cells, resistance to therapy frequently emerges from a small subset of those cells1-7. Molecular differences in rare individual cells in the initial population enable certain cells to become resistant to therapy7-9; however, comparatively little is known about the variability in the resistance outcomes. Here we develop and apply FateMap, a framework that combines DNA barcoding with single-cell RNA sequencing, to reveal the fates of hundreds of thousands of clones exposed to anti-cancer therapies. We show that resistant clones emerging from single-cell-derived cancer cells adopt molecularly, morphologically and functionally distinct resistant types. These resistant types are largely predetermined by molecular differences between cells before drug addition and not by extrinsic factors. Changes in the dose and type of drug can switch the resistant type of an initial cell, resulting in the generation and elimination of certain resistant types. Samples from patients show evidence for the existence of these resistant types in a clinical context. We observed diversity in resistant types across several single-cell-derived cancer cell lines and cell types treated with a variety of drugs. The diversity of resistant types as a result of the variability in intrinsic cell states may be a generic feature of responses to external cues.

Changes in chromatin accessibility are not concordant with transcriptional changes for single-factor perturbations.

A major goal in the field of transcriptional regulation is the mapping of changes in the binding of transcription factors to the resultant changes in gene expression. Recently, methods for measuring chromatin accessibility have enabled us to measure changes in accessibility across the genome, which are thought to correspond to transcription factor-binding events. In concert with RNA-sequencing, these data in principle enable such mappings; however, few studies have looked at their concordance over short-duration treatments with specific perturbations. Here, we used tandem, bulk ATAC-seq, and RNA-seq measurements from MCF-7 breast carcinoma cells to systematically evaluate the concordance between changes in accessibility and changes in expression in response to retinoic acid and TGF-ß. We found two classes of genes whose expression showed a significant change: those that showed some changes in the accessibility of nearby chromatin, and those that showed virtually no change despite strong changes in expression. The peaks associated with genes in the former group had lower baseline accessibility prior to exposure to signal. Focusing the analysis specifically on peaks with motifs for transcription factors associated with retinoic acid and TGF-ß signaling did not reduce the lack of correspondence. Analysis of paired chromatin accessibility and gene expression data from distinct paths along the hematopoietic differentiation trajectory showed a much stronger correspondence, suggesting that the multifactorial biological processes associated with differentiation may lead to changes in chromatin accessibility that reflect rather than driving altered transcriptional status. Together, these results show many gene expression changes can happen independently of changes in the accessibility of local chromatin in the context of a single-factor perturbation.

Joint profiling of chromatin accessibility and CAR-T integration site analysis at population and single-cell levels.

Chimeric antigen receptor (CAR)-T immunotherapy has yielded impressive results in several B cell malignancies, establishing itself as a powerful means to redirect the natural properties of T lymphocytes. In this strategy, the T cell genome is modified by the integration of lentiviral vectors encoding CAR that direct tumor cell killing. However, this therapeutic approach is often limited by the extent of CAR-T cell expansion in vivo. A major outstanding question is whether or not CAR-T integration itself enhances the proliferative competence of individual T cells by rewiring their regulatory landscape. To address this question, it is critical to define the identity of an individual CAR-T cell and simultaneously chart where the CAR-T vector integrates into the genome. Here, we report the development of a method called EpiVIA (https://github.com/VahediLab/epiVIA) for the joint profiling of the chromatin accessibility and lentiviral integration site analysis at the population and single-cell levels. We validate our technique in clonal cells with previously defined integration sites and further demonstrate the ability to measure lentiviral integration sites and chromatin accessibility of host and viral genomes at the single-cell resolution in CAR-T cells. We anticipate that EpiVIA will enable the single-cell deconstruction of gene regulation during CAR-T therapy, leading to the discovery of cellular factors associated with durable treatment.

Synthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms.

Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, "singletCode," to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.

Cell type determination for cardiac differentiation occurs soon after seeding of human-induced pluripotent stem cells.

BACKGROUND: Cardiac differentiation of human-induced pluripotent stem (hiPS) cells consistently produces a mixed population of cardiomyocytes and non-cardiac cell types, even when using well-characterized protocols. We sought to determine whether different cell types might result from intrinsic differences in hiPS cells prior to the onset of differentiation. RESULTS: By associating individual differentiated cells that share a common hiPS cell precursor, we tested whether expression variability is predetermined from the hiPS cell state. In a single experiment, cells that shared a progenitor were more transcriptionally similar to each other than to other cells in the differentiated population. However, when the same hiPS cells were differentiated in parallel, we did not observe high transcriptional similarity across differentiations. Additionally, we found that substantial cell death occurs during differentiation in a manner that suggested all cells were equally likely to survive or die, suggesting that there is no intrinsic selection bias for cells descended from particular hiPS cell progenitors. We thus wondered how cells grow spatially during differentiation, so we labeled cells by expression of marker genes and found that cells expressing the same marker tended to occur in patches. Our results suggest that cell type determination across multiple cell types, once initiated, is maintained in a cell-autonomous manner for multiple divisions. CONCLUSIONS: Altogether, our results show that while substantial heterogeneity exists in the initial hiPS cell population, it is not responsible for the variability observed in differentiated outcomes; instead, factors specifying the various cell types likely act during a window that begins shortly after the seeding of hiPS cells for differentiation.

A human liver cell-based system modeling a clinical prognostic liver signature for therapeutic discovery.

Chronic liver disease and hepatocellular carcinoma (HCC) are life-threatening diseases with limited treatment options. The lack of clinically relevant/tractable experimental models hampers therapeutic discovery. Here, we develop a simple and robust human liver cell-based system modeling a clinical prognostic liver signature (PLS) predicting long-term liver disease progression toward HCC. Using the PLS as a readout, followed by validation in nonalcoholic steatohepatitis/fibrosis/HCC animal models and patient-derived liver spheroids, we identify nizatidine, a histamine receptor H2 (HRH2) blocker, for treatment of advanced liver disease and HCC chemoprevention. Moreover, perturbation studies combined with single cell RNA-Seq analyses of patient liver tissues uncover hepatocytes and HRH2+, CLEC5Ahigh, MARCOlow liver macrophages as potential nizatidine targets. The PLS model combined with single cell RNA-Seq of patient tissues enables discovery of urgently needed targets and therapeutics for treatment of advanced liver disease and cancer prevention.

Transcriptional signature of human pro-inflammatory TH17 cells identifies reduced IL10 gene expression in multiple sclerosis.

We have previously reported the molecular signature of murine pathogenic TH17 cells that induce experimental autoimmune encephalomyelitis (EAE) in animals. Here we show that human peripheral blood IFN-γ+IL-17+ (TH1/17) and IFN-γ-IL-17+ (TH17) CD4+ T cells display distinct transcriptional profiles in high-throughput transcription analyses. Compared to TH17 cells, TH1/17 cells have gene signatures with marked similarity to mouse pathogenic TH17 cells. Assessing 15 representative signature genes in patients with multiple sclerosis, we find that TH1/17 cells have elevated expression of CXCR3 and reduced expression of IFNG, CCL3, CLL4, GZMB, and IL10 compared to healthy controls. Moreover, higher expression of IL10 in TH17 cells is found in clinically stable vs. active patients. Our results define the molecular signature of human pro-inflammatory TH17 cells, which can be used to both identify pathogenic TH17 cells and to measure the effect of treatment on TH17 cells in human autoimmune diseases.

Dopamine receptor 1 neurons in the dorsal striatum regulate food anticipatory circadian activity rhythms in mice.

Daily rhythms of food anticipatory activity (FAA) are regulated independently of the suprachiasmatic nucleus, which mediates entrainment of rhythms to light, but the neural circuits that establish FAA remain elusive. In this study, we show that mice lacking the dopamine D1 receptor (D1R KO mice) manifest greatly reduced FAA, whereas mice lacking the dopamine D2 receptor have normal FAA. To determine where dopamine exerts its effect, we limited expression of dopamine signaling to the dorsal striatum of dopamine-deficient mice; these mice developed FAA. Within the dorsal striatum, the daily rhythm of clock gene period2 expression was markedly suppressed in D1R KO mice. Pharmacological activation of D1R at the same time daily was sufficient to establish anticipatory activity in wild-type mice. These results demonstrate that dopamine signaling to D1R-expressing neurons in the dorsal striatum plays an important role in manifestation of FAA, possibly by synchronizing circadian oscillators that modulate motivational processes and behavioral output.