Retrospective identification of cell-intrinsic factors that mark pluripotency potential in rare somatic cells

Pluripotency can be induced in somatic cells by the expression of OCT4, KLF4, SOX2, and MYC. Usually only a rare subset of cells reprogram, and the molecular characteristics of this subset remain unknown. We apply retrospective clone tracing to identify and characterize the rare human fibroblasts primed for reprogramming. These fibroblasts showed markers of increased cell cycle speed and decreased fibroblast activation. Knockdown of a fibroblast activation factor identified by our analysis increased the reprogramming efficiency. We provide evidence for a unified model in which cells can move into and out of the primed state over time, explaining how reprogramming appears deterministic at short timescales and stochastic at long timescales. Furthermore, inhibiting the activity of LSD1 enlarged the pool of cells that were primed for reprogramming. Thus, even homogeneous cell populations can exhibit heritable molecular variability that can dictate whether individual rare cells will reprogram or not.

Disrupting cellular memory to overcome drug resistance

Gene expression states persist for varying lengths of time at the single-cell level, a phenomenon known as gene expression memory. When cells switch states, losing memory of their prior state, this transition can occur in the absence of genetic changes. However, we lack robust methods to find regulators of memory or track state switching. Here, we develop a lineage tracing-based technique to quantify memory and identify cells that switch states. Applied to melanoma cells without therapy, we quantify long-lived fluctuations in gene expression that are predictive of later resistance to targeted therapy. We also identify the PI3K and TGF-β pathways as state switching modulators. We propose a pretreatment model, first applying a PI3K inhibitor to modulate gene expression states, then applying targeted therapy, which leads to less resistance than targeted therapy alone. Together, we present a method for finding modulators of gene expression memory and their associated cell fates.

Principles of Computation by Competitive Protein Dimerization Networks

Many biological signaling pathways employ proteins that competitively dimerize in diverse combinations. These dimerization networks can perform biochemical computations, in which the concentrations of monomers (inputs) determine the concentrations of dimers (outputs). Despite their prevalence, little is known about the range of input-output computations that dimerization networks can perform (their "expressivity") and how it depends on network size and connectivity. Using a systematic computational approach, we demonstrate that even small dimerization networks (3-6 monomers) are expressive, performing diverse multi-input computations. Further, dimerization networks are versatile, performing different computations when their protein components are expressed at different levels, such as in different cell types. Remarkably, individual networks with random interaction affinities, when large enough (≥8 proteins), can perform nearly all (~90%) potential one-input network computations merely by tuning their monomer expression levels. Thus, even the simple process of competitive dimerization provides a powerful architecture for multi-input, cell-type-specific signal processing.

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.

Retrospective identification of intrinsic factors that mark pluripotency potential in rare somatic cells

Pluripotency can be induced in somatic cells by the expression of the four "Yamanaka" factors OCT4, KLF4, SOX2, and MYC. However, even in homogeneous conditions, usually only a rare subset of cells admit reprogramming, and the molecular characteristics of this subset remain unknown. Here, we apply retrospective clone tracing to identify and characterize the individual human fibroblast cells that are primed for reprogramming. These fibroblasts showed markers of increased cell cycle speed and decreased fibroblast activation. Knockdown of a fibroblast activation factor identified by our analysis led to increased reprogramming efficiency, identifying it as a barrier to reprogramming. Changing the frequency of reprogramming by inhibiting the activity of LSD1 led to an enlarging of the pool of cells that were primed for reprogramming. Our results show that even homogeneous cell populations can exhibit heritable molecular variability that can dictate whether individual rare cells will reprogram or not.

3D genome organization around nuclear speckles drives mRNA splicing efficiency

The nucleus is highly organized such that factors involved in transcription and processing of distinct classes of RNA are organized within specific nuclear bodies. One such nuclear body is the nuclear speckle, which is defined by high concentrations of protein and non-coding RNA regulators of pre-mRNA splicing. What functional role, if any, speckles might play in the process of mRNA splicing remains unknown. Here we show that genes localized near nuclear speckles display higher spliceosome concentrations, increased spliceosome binding to their pre-mRNAs, and higher co-transcriptional splicing levels relative to genes that are located farther from nuclear speckles. We show that directed recruitment of a pre-mRNA to nuclear speckles is sufficient to drive increased mRNA splicing levels. Finally, we show that gene organization around nuclear speckles is highly dynamic with differential localization between cell types corresponding to differences in Pol II occupancy. Together, our results integrate the longstanding observations of nuclear speckles with the biochemistry of mRNA splicing and demonstrate a critical role for dynamic 3D spatial organization of genomic DNA in driving spliceosome concentrations and controlling the efficiency of mRNA splicing.

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.

Abstract LB-050: Memory sequencing reveals heritable single cell gene expression programs associated with therapy resistance

Single cancer cells can have substantial fluctuations in gene expression from non-genetic factors. However, it is unclear how long these fluctuations exist and whether they extend beyond the length of a single-cell division. Current technologies for gene expression measurements in single cells provide just one measurement of each cells’ transcriptome and fail to capture how these gene expression fluctuations change with time. We developed a new method combining Luria and Delbruck's fluctuation analysis with population-based RNA sequencing (MemorySeq) for identifying genes transcriptome-wide whose fluctuations persist for several cell divisions. MemorySeq revealed multiple gene modules that express together in rare cells within otherwise homogeneous clonal populations. Further, we found that these rare cell subpopulations are associated with biologically distinct phenotypes in multiple different cancer cell lines. In melanoma, we identified a rare subpopulation of cells expressing EGFR, NGFR, and AXL, and demonstrated that these cells are resistant to BRAF and MEK inhibitors. In triple-negative breast cancer cells, we identified a different gene expression signature consisting of CA9, and found that this subpopulation is resistant to paclitaxol. We validated these findings in melanoma cell lines by tagging the endogenous locus of MemorySeq gene, NGFR, with a fluorescent protein to directly visualize gene expression fluctuations in individual cells. Broadly, we believe that memory is an essential property of variability that will have phenotypic consequences for cancer cells. Thus, we anticipate that MemorySeq can be extended to uncover the transcriptional states of rare cells driving many phenotypes in cancer including drug resistance, differentiation, invasion, and metastasis.

Citation Format: Sydney Shaffer, Benjamin Emert, Eduardo Torre, Arjun Raj. Memory sequencing reveals heritable single cell gene expression programs associated with therapy resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-050.

ClampFISH detects individual nucleic acid molecules using click chemistry-based amplification

Methods for detecting single nucleic acids in cell and tissues, such as fluorescence in situ hybridization (FISH), are limited by relatively low signal intensity and nonspecific probe binding. Here we present click-amplifying FISH (clampFISH), a method for fluorescence detection of nucleic acids that achieves high specificity and high-gain (>400-fold) signal amplification. ClampFISH probes form a 'C' configuration upon hybridization to the sequence of interest in a double helical manner. The ends of the probes are ligated together using bio-orthogonal click chemistry, effectively locking the probes around the target. Iterative rounds of hybridization and click amplify the fluorescence intensity. We show that clampFISH enables the detection of RNA species with low-magnification microscopy and in RNA-based flow cytometry. Additionally, we show that the modular design of clampFISH probes allows multiplexing of RNA and DNA detection, that the locking mechanism prevents probe detachment in expansion microscopy, and that clampFISH can be applied in tissue samples.

Abstract 2837: Single-cell heterogeneity with transcriptional memory confers resistance to cancer therapy

Targeted therapies for cancer are a promising class of drugs that inhibit the specific molecular alterations that underlie the uncontrolled proliferation seen in cancer. The primary shortcoming of targeted therapy is disease relapse, which is driven by a subpopulation of cells that are resistant to these drugs. This phenomenon is generally thought to be genetic in origin; however, our recent work on melanoma shows that nongenetic cellular plasticity may provide a mechanism of resistance to these therapies. Furthermore, we showed that through the addition of the drug itself, cells transition from this transient plasticity into a new, stably resistant cell state via cellular reprogramming, suggesting that the time an individual cell exists in a state is important for producing the divergent resistance phenotype. However, there are currently no methods available to quantify the timescale of these fluctuations for the whole transcriptome. Thus, broadly generalizing this concept of timescales for cellular plasticity, we developed a novel method for genome-wide quantification of the timescales of gene expression memory based on a modern version of the ingenious Luria-Delbrück fluctuation analysis. Specifically, we isolated individual cells, allowed them to expand, and then performed RNA sequencing on each of the samples. For genes with uniform expression, all the samples should have similar RNA sequencing counts; however, for genes that occasionally turn on in a subset of cells, the RNA sequencing counts across all samples should show high variance. In melanoma, this method revealed the gene expression state of rare cells resistant to targeted therapy. In a completely new model, triple-negative breast cancer, this method revealed a novel rare subpopulation of cells exhibiting resistance to chemotherapy. More generally, this method has the potential to reveal other new phenotypes associated with rare cell biology, including metastasis and the early stages of stem cell differentiation.

Citation Format: Sydney Shaffer, Benjamin Emert, Arjun Raj. Single-cell heterogeneity with transcriptional memory confers resistance to cancer therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2837.

Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance

Therapies targeting signaling molecules mutated in cancers can often have striking short-term effects, but the emergence of resistant cancer cells is a major barrier to full cures^1,2. Resistance can result from a secondary mutations^3,4, but other times there is no clear genetic cause, raising the possibility of non-genetic rare cell variability^5–11. Here, we show that melanoma cells can display profound transcriptional variability at the single cell level that predicts which cells will ultimately resist drug treatment. This variability involves infrequent, semi-coordinated transcription of a number of resistance markers at high levels in a very small percentage of cells. The addition of drug then induces epigenetic reprogramming in these cells, converting the transient transcriptional state to a stably resistant state. This reprogramming begins with a loss of SOX10-mediated differentiation followed by activation of new signaling pathways, partially mediated by activity of Jun-AP-1 and TEAD. Our work reveals the multistage nature of the acquisition of drug resistance and provides a framework for understanding resistance dynamics in single cells. We find that other cell types also exhibit sporadic expression of many of these same marker genes, suggesting the existence of a general rare-cell expression program.

Genetic and environmental control of host-gut microbiota interactions

Genetics provides a potentially powerful approach to dissect host-gut microbiota interactions. Toward this end, we profiled gut microbiota using 16s rRNA gene sequencing in a panel of 110 diverse inbred strains of mice. This panel has previously been studied for a wide range of metabolic traits and can be used for high-resolution association mapping. Using a SNP-based approach with a linear mixed model, we estimated the heritability of microbiota composition. We conclude that, in a controlled environment, the genetic background accounts for a substantial fraction of abundance of most common microbiota. The mice were previously studied for response to a high-fat, high-sucrose diet, and we hypothesized that the dietary response was determined in part by gut microbiota composition. We tested this using a cross-fostering strategy in which a strain showing a modest response, SWR, was seeded with microbiota from a strain showing a strong response, A×B19. Consistent with a role of microbiota in dietary response, the cross-fostered SWR pups exhibited a significantly increased response in weight gain. To examine specific microbiota contributing to the response, we identified various genera whose abundance correlated with dietary response. Among these, we chose Akkermansia muciniphila, a common anaerobe previously associated with metabolic effects. When administered to strain A×B19 by gavage, the dietary response was significantly blunted for obesity, plasma lipids, and insulin resistance. In an effort to further understand host-microbiota interactions, we mapped loci controlling microbiota composition and prioritized candidate genes. Our publicly available data provide a resource for future studies.

HDL inhibits the effects of oxidized phospholipids on endothelial cell gene expression via multiple mechanisms

Oxidized 1-palmitoyl-2-arachidonyl-sn-glycero-3-phospholcholine (OxPAPC) and its component phospholipids accumulate in atherosclerotic lesions and regulate the expression of >1,000 genes, many proatherogenic, in human aortic endothelial cells (HAECs). In contrast, there is evidence in the literature that HDL protects the vasculature from inflammatory insult. We have previously shown that in HAECs, HDL attenuates the expression of several proatherogenic genes regulated by OxPAPC and 1-palmitoyl-2-(5,6-epoxyisoprostane E2)-sn-glycero-3-phosphocholine. We now demonstrate that HDL reverses >50% of the OxPAPC transcriptional response. Genes reversed by HDL are enriched for inflammatory and vascular development pathways, while genes not affected by HDL are enriched for oxidative stress response pathways. The protective effect of HDL is partially mimicked by cholesterol repletion and treatment with apoA1 but does not require signaling through scavenger receptor class B type I. Furthermore, our data demonstrate that HDL protection requires direct interaction with OxPAPC. HDL-associated platelet-activating factor acetylhydrolase (PAF-AH) hydrolyzes short-chain bioactive phospholipids in OxPAPC; however, inhibiting PAF-AH activity does not prevent HDL protection. Our results are consistent with HDL sequestering specific bioactive lipids in OxPAPC, thereby preventing their regulation of select target genes. Overall, this work implicates HDL as a major regulator of OxPAPC action in endothelial cells via multiple mechanisms.

An epoxyisoprostane is a major regulator of endothelial cell function

The goal of these studies was to determine the effect of 5,6-epoxyisoprostane, EI, on human aortic endothelial cells (HAEC). EI can form as a phospholipase product of 1-palmitoyl-2-(5,6-epoxyisoprostane E2)-sn-glycero-3-phosphocholine, PEIPC, a proinflammatory molecule that accumulates in sites of inflammation where phospholipases are also increased. To determine the effect of EI on HAEC, we synthesized several stereoisomers of EI using a convergent approach from the individual optically pure building blocks, the epoxyaldehydes 5 and 6 and the bromoenones 14 and 16. The desired stereoisomer of EI can be prepared from these materials in only six operations, and thus, large amounts of the product can be obtained. The trans/trans isomers had the most potent activity, suggesting specificity in the interaction of EI with the cell surface. EI has potent anti-inflammatory effects in HAEC. EI strongly inhibits the production of MCP-1, a major monocyte chemotactic factor, and either decreases or minimally increases the levels of 10 proinflammatory molecules increased by PEIPC. EI also strongly down-regulates the inflammatory effects of IL-1β, a major inflammatory cytokine. Thus EI, a hydrolytic product of PEIPC, has potent anti-inflammatory function.