Development of a functional beige fat cell line uncovers independent subclasses of cells expressing UCP1 and the futile creatine cycle.

Although uncoupling protein 1 (UCP1) is established as a major contributor to adipose thermogenesis, recent data have illustrated an important role for alternative pathways, particularly the futile creatine cycle (FCC). How these pathways co-exist in cells and tissues has not been explored. Beige cell adipogenesis occurs in vivo but has been difficult to model in vitro; here, we describe the development of a murine beige cell line that executes a robust respiratory response, including uncoupled respiration and the FCC. The key FCC enzyme, tissue-nonspecific alkaline phosphatase (TNAP), is localized almost exclusively to mitochondria in these cells. Surprisingly, single-cell cloning from this cell line shows that cells with the highest levels of UCP1 express little TNAP, and cells with the highest expression of TNAP express little UCP1. Immunofluorescence analysis of subcutaneous fat from cold-exposed mice confirms that the highest levels of these critical thermogenic components are expressed in distinct fat cell populations.

Clonal differences underlie variable responses to sequential and prolonged treatment.

Cancer cells exhibit dramatic differences in gene expression at the single-cell level, which can predict whether they become resistant to treatment. Treatment perpetuates this heterogeneity, resulting in a diversity of cell states among resistant clones. However, it remains unclear whether these differences lead to distinct responses when another treatment is applied or the same treatment is continued. In this study, we combined single-cell RNA sequencing with barcoding to track resistant clones through prolonged and sequential treatments. We found that cells within the same clone have similar gene expression states after multiple rounds of treatment. Moreover, we demonstrated that individual clones have distinct and differing fates, including growth, survival, or death, when subjected to a second treatment or when the first treatment is continued. By identifying gene expression states that predict clone survival, this work provides a foundation for selecting optimal therapies that target the most aggressive resistant clones within a tumor. A record of this paper's transparent peer review process is included in the supplemental information.

Breaking reflection symmetry: evolving long dynamical cycles in Boolean systems.

In interacting dynamical systems, specific local interaction rules for system components give rise to diverse and complex global dynamics. Long dynamical cycles are a key feature of many natural interacting systems, especially in biology. Examples of dynamical cycles range from circadian rhythms regulating sleep to cell cycles regulating reproductive behavior. Despite the crucial role of cycles in nature, the properties of network structure that give rise to cycles still need to be better understood. Here, we use a Boolean interaction network model to study the relationships between network structure and cyclic dynamics. We identify particular structural motifs that support cycles, and other motifs that suppress them. More generally, we show that the presence of dynamical reflection symmetry in the interaction network enhances cyclic behavior. In simulating an artificial evolutionary process, we find that motifs that break reflection symmetry are discarded. We further show that dynamical reflection symmetries are over-represented in Boolean models of natural biological systems. Altogether, our results demonstrate a link between symmetry and functionality for interacting dynamical systems, and they provide evidence for symmetry's causal role in evolving dynamical functionality.

IGF2BP1/IMP1 Deletion Enhances a Facultative Stem Cell State via Regulation of MAP1LC3B.

BACKGROUND & AIMS: The intestinal epithelium interfaces with a diverse milieu of luminal contents while maintaining robust digestive and barrier functions. Facultative intestinal stem cells are cells that survive tissue injury and divide to re-establish the epithelium. Prior studies have shown autophagic state as functional marker of facultative intestinal stem cells, but regulatory mechanisms are not known. The current study evaluated a post-transcriptional regulation of autophagy as an important factor for facultative stem cell state and tissue regeneration. METHODS: We evaluated stem cell composition, autophagic vesicle content, organoid formation, and in vivo regeneration in mice with intestinal epithelial deletion of the RNA binding protein IGF2 messenger RNA binding protein 1 (IMP1). The contribution of autophagy to resulting in vitro and in vivo phenotypes was evaluated via genetic inactivation of Atg7. Molecular analyses of IMP1 modulation of autophagy at the protein and transcript localization levels were performed using IMP1 mutant studies and single-molecule fluorescent in situ hybridization. RESULTS: Epithelial Imp1 deletion reduced leucine rich repeat containing G protein coupled receptor 5 cell frequency but enhanced both organoid formation efficiency and in vivo regeneration after irradiation. We confirmed prior studies showing increased autophagy with IMP1 deletion. Deletion of Atg7 reversed the enhanced regeneration observed with Imp1 deletion. IMP1 deletion or mutation of IMP1 phosphorylation sites enhanced expression of essential autophagy protein microtubule-associated protein 1 light chain 3ß. Furthermore, immunofluorescence imaging coupled with single-molecule fluorescent in situ hybridization showed IMP1 colocalization with MAP1LC3B transcripts at homeostasis. Stress induction led to decreased colocalization. CONCLUSIONS: Depletion of IMP1 enhances autophagy, which promotes intestinal regeneration via expansion of facultative intestinal stem cells.

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.

Clonal differences underlie variable responses to sequential and prolonged treatment.

Cancer cells exhibit dramatic differences in gene expression at the single-cell level which can predict whether they become resistant to treatment. Treatment perpetuates this heterogeneity, resulting in a diversity of cell states among resistant clones. However, it remains unclear whether these differences lead to distinct responses when another treatment is applied or the same treatment is continued. In this study, we combined single-cell RNA-sequencing with barcoding to track resistant clones through prolonged and sequential treatments. We found that cells within the same clone have similar gene expression states after multiple rounds of treatment. Moreover, we demonstrated that individual clones have distinct and differing fates, including growth, survival, or death, when subjected to a second treatment or when the first treatment is continued. By identifying gene expression states that predict clone survival, this work provides a foundation for selecting optimal therapies that target the most aggressive resistant clones within a tumor.

Clonal cell states link Barrett's esophagus and esophageal adenocarcinoma.

Barrett's esophagus is a common type of metaplasia and a precursor of esophageal adenocarcinoma. However, the cell states and lineage connections underlying the origin, maintenance, and progression of Barrett's esophagus have not been resolved in humans. To address this, we performed single-cell lineage tracing and transcriptional profiling of patient cells isolated from metaplastic and healthy tissue. Our analysis revealed discrete lineages in Barrett's esophagus, normal esophagus, and gastric cardia. Transitional basal progenitor cells of the gastroesophageal junction were unexpectedly related to both esophagus and gastric cardia cells. Barrett's esophagus was polyclonal, with lineages that contained all progenitor and differentiated cell types. In contrast, precancerous dysplastic foci were initiated by the expansion of a single molecularly aberrant Barrett's esophagus clone. Together, these findings provide a comprehensive view of the cell dynamics of Barrett's esophagus, linking cell states along the full disease trajectory, from its origin to cancer.

E-cigarette use and respiratory symptoms in residents of the United States: A BRFSS report.

PURPOSE: E-cigarettes are the most common type of electronic nicotine delivery system in the United States. E-cigarettes contain numerous toxic compounds that has been shown to induce severe structural damage to the airways. The objective of this study is to assess if there is an association between e-cigarette use and respiratory symptoms in adults in the US as reported in the BRFSS. METHODS: We analyzed data from 18,079 adults, 18-44 years, who participated at the Behavioral Risk Factor Surveillance System (BRFSS) in the year 2017. E-cigarette smoking status was categorized as current everyday user, current some days user, former smoker, and never smoker. The frequency of any respiratory symptoms (cough, phlegm, or shortness of breath) was compared. Unadjusted and adjusted logistic regression analysis were used to calculate odds ratios (OR) and 95% confidence intervals (CI). RESULTS: The BRFSS reported prevalence of smoking e-cigarettes was 6%. About 28% of the participants reported any of the respiratory symptoms assessed. The frequency of reported respiratory symptoms was highest among current some days e-cigarette users (45%). After adjusting for selected participant's demographic, socio-economic, and behavioral characteristics, and asthma and COPD status, the odds of reporting respiratory symptoms increased by 49% among those who use e-cigarettes some days (OR 1.49; 95% CI: 1.06-2.11), and by 29% among those who were former users (OR 1.29; 95% CI: 1.07-1.55) compared with those who never used e-cigarettes. No statistically significant association was found for those who used e-cigarettes every day (OR 1.41; 95% CI 0.96-2.08). CONCLUSION: E-cigarettes cannot be considered as a safe alternative to aid quitting use of combustible traditional cigarettes. Cohort studies may shed more evidence on the association between e-cigarette use and respiratory diseases.

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.

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.

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.

TooManyCells identifies and visualizes relationships of single-cell clades.

Identifying and visualizing transcriptionally similar cells is instrumental for accurate exploration of the cellular diversity revealed by single-cell transcriptomics. However, widely used clustering and visualization algorithms produce a fixed number of cell clusters. A fixed clustering 'resolution' hampers our ability to identify and visualize echelons of cell states. We developed TooManyCells, a suite of graph-based algorithms for efficient and unbiased identification and visualization of cell clades. TooManyCells introduces a visualization model built on a concept intentionally orthogonal to dimensionality-reduction methods. TooManyCells is also equipped with an efficient matrix-free divisive hierarchical spectral clustering different from prevalent single-resolution clustering methods. TooManyCells enables multiresolution and multifaceted exploration of single-cell clades. An advantage of this paradigm is the immediate detection of rare and common populations that outperforms popular clustering and visualization algorithms, as demonstrated using existing single-cell transcriptomic data sets and new data modeling drug-resistance acquisition in leukemic T cells.

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.

Transcriptional Bursting Explains the Noise-Versus-Mean Relationship in mRNA and Protein Levels.

Recent analysis demonstrates that the HIV-1 Long Terminal Repeat (HIV LTR) promoter exhibits a range of possible transcriptional burst sizes and frequencies for any mean-expression level. However, these results have also been interpreted as demonstrating that cell-to-cell expression variability (noise) and mean are uncorrelated, a significant deviation from previous results. Here, we re-examine the available mRNA and protein abundance data for the HIV LTR and find that noise in mRNA and protein expression scales inversely with the mean along analytically predicted transcriptional burst-size manifolds. We then experimentally perturb transcriptional activity to test a prediction of the multiple burst-size model: that increasing burst frequency will cause mRNA noise to decrease along given burst-size lines as mRNA levels increase. The data show that mRNA and protein noise decrease as mean expression increases, supporting the canonical inverse correlation between noise and mean.