Memory Sequencing Reveals Heritable Single-Cell Gene Expression Programs Associated with Distinct Cellular Behaviors.

Non-genetic factors can cause individual cells to fluctuate substantially in gene expression levels over time. It remains unclear whether these fluctuations can persist for much longer than the time of one cell division. Current methods for measuring gene expression in single cells mostly rely on single time point measurements, making the duration of gene expression fluctuations or cellular memory difficult to measure. Here, we combined Luria and Delbrück's fluctuation analysis with population-based RNA sequencing (MemorySeq) for identifying genes transcriptome-wide whose fluctuations persist for several divisions. MemorySeq revealed multiple gene modules that expressed together in rare cells within otherwise homogeneous clonal populations. These rare cell subpopulations were associated with biologically distinct behaviors like proliferation in the face of anti-cancer therapeutics. The identification of non-genetic, multigenerational fluctuations can reveal new forms of biological memory in single cells and suggests that non-genetic heritability of cellular state may be a quantitative property.

Genome organization around nuclear speckles drives mRNA splicing efficiency.

The nucleus is highly organized, such that factors involved in the transcription and processing of distinct classes of RNA are confined within specific nuclear bodies1,2. One example is the nuclear speckle, which is defined by high concentrations of protein and noncoding RNA regulators of pre-mRNA splicing3. What functional role, if any, speckles might play in the process of mRNA splicing is unclear4,5. 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 than genes that are located farther from nuclear speckles. Gene organization around nuclear speckles is dynamic between cell types, and changes in speckle proximity lead to differences in splicing efficiency. Finally, directed recruitment of a pre-mRNA to nuclear speckles is sufficient to increase mRNA splicing levels. Together, our results integrate the long-standing observations of nuclear speckles with the biochemistry of mRNA splicing and demonstrate a crucial role for dynamic three-dimensional spatial organization of genomic DNA in driving spliceosome concentrations and controlling the efficiency of mRNA splicing.

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.

ClampFISH 2.0 enables rapid, scalable amplified RNA detection in situ.

RNA labeling in situ has enormous potential to visualize transcripts and quantify their levels in single cells, but it remains challenging to produce high levels of signal while also enabling multiplexed detection of multiple RNA species simultaneously. Here, we describe clampFISH 2.0, a method that uses an inverted padlock design to efficiently detect many RNA species and exponentially amplify their signals at once, while also reducing the time and cost compared with the prior clampFISH method. We leverage the increased throughput afforded by multiplexed signal amplification and sequential detection to detect 10 different RNA species in more than 1 million cells. We also show that clampFISH 2.0 works in tissue sections. We expect that the advantages offered by clampFISH 2.0 will enable many applications in spatial transcriptomics.

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

This corrects the article DOI: 10.1038/nature22794.

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

Therapies that target signalling molecules that are mutated in cancers can often have substantial short-term effects, but the emergence of resistant cancer cells is a major barrier to full cures. Resistance can result from secondary mutations, but in other cases there is no clear genetic cause, raising the possibility of non-genetic rare cell variability. Here we show that human 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 signalling pathways, partially mediated by the activity of the transcription factors JUN and/or 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 program in which expression is displayed in rare subpopulations of cells.

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.

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.

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.

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.

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.

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-ß 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.

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.

Variability within rare cell states enables multiple paths toward drug resistance.

Molecular differences between individual cells can lead to dramatic differences in cell fate, such as death versus survival of cancer cells upon drug treatment. These originating differences remain largely hidden due to difficulties in determining precisely what variable molecular features lead to which cellular fates. Thus, we developed Rewind, a methodology that combines genetic barcoding with RNA fluorescence in situ hybridization to directly capture rare cells that give rise to cellular behaviors of interest. Applying Rewind to BRAFV600E melanoma, we trace drug-resistant cell fates back to single-cell gene expression differences in their drug-naive precursors (initial frequency of ~1:1,000-1:10,000 cells) and relative persistence of MAP kinase signaling soon after drug treatment. Within this rare subpopulation, we uncover a rich substructure in which molecular differences among several distinct subpopulations predict future differences in phenotypic behavior, such as proliferative capacity of distinct resistant clones after drug treatment. Our results reveal hidden, rare-cell variability that underlies a range of latent phenotypic outcomes upon drug exposure.

Genetic screening for single-cell variability modulators driving therapy resistance.

Cellular plasticity describes the ability of cells to transition from one set of phenotypes to another. In melanoma, transient fluctuations in the molecular state of tumor cells mark the formation of rare cells primed to survive BRAF inhibition and reprogram into a stably drug-resistant fate. However, the biological processes governing cellular priming remain unknown. We used CRISPR-Cas9 genetic screens to identify genes that affect cell fate decisions by altering cellular plasticity. We found that many factors can independently affect cellular priming and fate decisions. We discovered a new plasticity-based mode of increasing resistance to BRAF inhibition that pushes cells towards a more differentiated state. Manipulating cellular plasticity through inhibition of DOT1L before the addition of the BRAF inhibitor resulted in more therapy resistance than concurrent administration. Our results indicate that modulating cellular plasticity can alter cell fate decisions and may prove useful for treating drug resistance in other cancers.

Subcellular Detection of SARS-CoV-2 RNA in Human Tissue Reveals Distinct Localization in Alveolar Type 2 Pneumocytes and Alveolar Macrophages.

The widespread coronavirus disease 2019 (COVID-19) is caused by infection with the novel coronavirus SARS-CoV-2. Currently, we have limited understanding of which cells become infected with SARS-CoV-2 in human tissues and where viral RNA localizes on the subcellular level. Here, we present a platform for preparing autopsy tissue for visualizing SARS-CoV-2 RNA using RNA fluorescence in situ hybridization (FISH) with amplification by hybridization chain reaction. We developed probe sets that target different regions of SARS-CoV-2 (including ORF1a and N), as well as probe sets that specifically target SARS-CoV-2 subgenomic mRNAs. We validated these probe sets in cell culture and tissues (lung, lymph node, and placenta) from infected patients. Using this technology, we observe distinct subcellular localization patterns of the ORF1a and N regions. In human lung tissue, we performed multiplexed RNA FISH HCR for SARS-CoV-2 and cell-type-specific marker genes. We found viral RNA in cells containing the alveolar type 2 (AT2) cell marker gene (SFTPC) and the alveolar macrophage marker gene (MARCO) but did not identify viral RNA in cells containing the alveolar type 1 (AT1) cell marker gene (AGER). Moreover, we observed distinct subcellular localization patterns of viral RNA in AT2 cells and alveolar macrophages. In sum, we demonstrate the use of RNA FISH HCR for visualizing different RNA species from SARS-CoV-2 in cell lines and FFPE (formalin fixation and paraffin embedding) autopsy specimens. We anticipate that this platform could be broadly useful for studying SARS-CoV-2 pathology in tissues, as well as extended for other applications, including investigating the viral life cycle, viral diagnostics, and drug screening. IMPORTANCE Here, we developed an in situ RNA detection assay for RNA generated by the SARS-CoV-2 virus. We found viral RNA in lung, lymph node, and placenta samples from pathology specimens from COVID patients. Using high-magnification microscopy, we can visualize the subcellular distribution of these RNA in single cells.

Multiplexed detection of SARS-CoV-2 genomic and subgenomic RNA using in situ hybridization.

The widespread Coronavirus Disease 2019 (COVID-19) is caused by infection with the novel coronavirus SARS-CoV-2. Currently, we have a limited toolset available for visualizing SARS-CoV-2 in cells and tissues, particularly in tissues from patients who died from COVID-19. Generally, single-molecule RNA FISH techniques have shown mixed results in formalin fixed paraffin embedded tissues such as those preserved from human autopsies. Here, we present a platform for preparing autopsy tissue for visualizing SARS-CoV-2 RNA using RNA FISH with amplification by hybridization chain reaction (HCR). We developed probe sets that target different regions of SARS-CoV-2 (including ORF1a and N) as well as probe sets that specifically target SARS-CoV-2 subgenomic mRNAs. We validated these probe sets in cell culture and tissues (lung, lymph node, and placenta) from infected patients. Using this technology, we observe distinct subcellular localization patterns of the ORF1a and N regions, with the ORF1a concentrated around the nucleus and the N showing a diffuse distribution across the cytoplasm. In human lung tissue, we performed multiplexed RNA FISH HCR for SARS-CoV-2 and cell-type specific marker genes. We found viral RNA in cells containing the alveolar type 2 (AT2) cell marker gene (SFTPC) and the alveolar macrophage marker gene (MARCO), but did not identify viral RNA in cells containing the alveolar type 1 (AT1) cell marker gene (AGER). Moreover, we observed distinct subcellular localization patterns of viral RNA in AT2 cells and alveolar macrophages, consistent with phagocytosis of infected cells. In sum, we demonstrate the use of RNA FISH HCR for visualizing different RNA species from SARS-CoV-2 in cell lines and FFPE autopsy specimens. Furthermore, we multiplex this assay with probes for cellular genes to determine what cell-types are infected within the lung. We anticipate that this platform could be broadly useful for studying SARS-CoV-2 pathology in tissues as well as extended for other applications including investigating the viral life cycle, viral diagnostics, and drug screening.

Gene regulation gravitates toward either addition or multiplication when combining the effects of two signals.

Two different cell signals often affect transcription of the same gene. In such cases, it is natural to ask how the combined transcriptional response compares to the individual responses. The most commonly used mechanistic models predict additive or multiplicative combined responses, but a systematic genome-wide evaluation of these predictions is not available. Here, we analyzed the transcriptional response of human MCF-7 cells to retinoic acid and TGF-ß, applied individually and in combination. The combined transcriptional responses of induced genes exhibited a range of behaviors, but clearly favored both additive and multiplicative outcomes. We performed paired chromatin accessibility measurements and found that increases in accessibility were largely additive. There was some association between super-additivity of accessibility and multiplicative or super-multiplicative combined transcriptional responses, while sub-additivity of accessibility associated with additive transcriptional responses. Our findings suggest that mechanistic models of combined transcriptional regulation must be able to reproduce a range of behaviors.

Gene Networks with Transcriptional Bursting Recapitulate Rare Transient Coordinated High Expression States in Cancer.

Non-genetic transcriptional variability is a potential mechanism for therapy resistance in melanoma. Specifically, rare subpopulations of cells occupy a transient pre-resistant state characterized by coordinated high expression of several genes and survive therapy. How might these rare states arise and disappear within the population? It is unclear whether the canonical models of probabilistic transcriptional pulsing can explain this behavior, or if it requires special, hitherto unidentified mechanisms. We show that a minimal model of transcriptional bursting and gene interactions can give rise to rare coordinated high expression states. These states occur more frequently in networks with low connectivity and depend on three parameters. While entry into these states is initiated by a long transcriptional burst that also triggers entry of other genes, the exit occurs through independent inactivation of individual genes. Together, we demonstrate that established principles of gene regulation are sufficient to describe this behavior and argue for its more general existence. A record of this paper's transparent peer review process is included in the Supplemental Information.