Pathologic polyglutamine aggregation begins with a self-poisoning polymer crystal.

A long-standing goal of amyloid research has been to characterize the structural basis of the rate-determining nucleating event. However, the ephemeral nature of nucleation has made this goal unachievable with existing biochemistry, structural biology, and computational approaches. Here, we addressed that limitation for polyglutamine (polyQ), a polypeptide sequence that causes Huntington's and other amyloid-associated neurodegenerative diseases when its length exceeds a characteristic threshold. To identify essential features of the polyQ amyloid nucleus, we used a direct intracellular reporter of self-association to quantify frequencies of amyloid appearance as a function of concentration, conformational templates, and rational polyQ sequence permutations. We found that nucleation of pathologically expanded polyQ involves segments of three glutamine (Q) residues at every other position. We demonstrate using molecular simulations that this pattern encodes a four-stranded steric zipper with interdigitated Q side chains. Once formed, the zipper poisoned its own growth by engaging naive polypeptides on orthogonal faces, in a fashion characteristic of polymer crystals with intramolecular nuclei. We further show that self-poisoning can be exploited to block amyloid formation, by genetically oligomerizing polyQ prior to nucleation. By uncovering the physical nature of the rate-limiting event for polyQ aggregation in cells, our findings elucidate the molecular etiology of polyQ diseases.

Diseases that typically occur later in life, such as Alzheimer's, are often caused by specific proteins clumping together into structures known as amyloids. Once the process starts, amyloids will continue to form, leading to worse symptoms that cannot be cured. The best way to treat these diseases is therefore to stop amyloids from arising in the first place. Amyloids initially develop by proteins coming together to create an unstable structure referred to as the nucleus. The instability of the nucleus means it cannot be observed directly, making it hard to study this nucleation process. To overcome this, Kandola, Venkatesan et al. investigated the simplest protein known to form an amyloid ­ polyglutamine, which is made up of a chain of repeating building blocks known as amino acids. Polyglutamine forms only one type of amyloid which is associated with nine neurodegenerative diseases, including Huntington's disease. However, it only does this when its chain of amino acids exceeds a certain length, suggesting that a specific structure may be required for nucleation to begin. Kandola, Venkatesan et al. made alternative versions of the polyglutamine protein which each contained slightly different sequences of amino acids that will alter the way the protein folds. They then tested how well these different variants could form amyloids in yeast cells. This revealed that in order to join together into a nucleus, polyglutamine needs to be able to fold into a zipper shape made up of four interlocking strands. The length of the protein required to form this shape is also the same length that causes the amyloid associated with neurodegenerative diseases. Kandola, Venkatesan et al. also found that polyglutamine tends to bind to nuclei that have already formed in a way that hinders their growth. This 'self-poisoning' affect could potentially be exploited as a way to pre-emptively stop amyloids from initially arising. These findings have uncovered a potential therapeutic strategy for blocking amyloid formation that could eventually benefit people with or at risk of developing neurodegenerative diseases linked to polyglutamine. Additionally, this approach provides a blueprint for understanding how other proteins undergo amyloid nucleation, including those responsible for Alzheimer's, Parkinson's, and other diseases.

Pathologic polyglutamine aggregation begins with a self-poisoning polymer crystal.

A long-standing goal of amyloid research has been to characterize the structural basis of the rate-determining nucleating event. However, the ephemeral nature of nucleation has made this goal unachievable with existing biochemistry, structural biology, and computational approaches. Here, we addressed that limitation for polyglutamine (polyQ), a polypeptide sequence that causes Huntington's and other amyloid-associated neurodegenerative diseases when its length exceeds a characteristic threshold. To identify essential features of the polyQ amyloid nucleus, we used a direct intracellular reporter of self-association to quantify frequencies of amyloid appearance as a function of concentration, conformational templates, and rational polyQ sequence permutations. We found that nucleation of pathologically expanded polyQ involves segments of three glutamine (Q) residues at every other position. We demonstrate using molecular simulations that this pattern encodes a four-stranded steric zipper with interdigitated Q side chains. Once formed, the zipper poisoned its own growth by engaging naive polypeptides on orthogonal faces, in a fashion characteristic of polymer crystals with intramolecular nuclei. We further show that self-poisoning can be exploited to block amyloid formation, by genetically oligomerizing polyQ prior to nucleation. By uncovering the physical nature of the rate-limiting event for polyQ aggregation in cells, our findings elucidate the molecular etiology of polyQ diseases.

Identifying dynamical persistent biomarker structures for rare events using modern integrative machine learning approach.

The evolution of omics and computational competency has accelerated discoveries of the underlying biological processes in an unprecedented way. High throughput methodologies, such as flow cytometry, can reveal deeper insights into cell processes, thereby allowing opportunities for scientific discoveries related to health and diseases. However, working with cytometry data often imposes complex computational challenges due to high-dimensionality, large size, and nonlinearity of the data structure. In addition, cytometry data frequently exhibit diverse patterns across biomarkers and suffer from substantial class imbalances which can further complicate the problem. The existing methods of cytometry data analysis either predict cell population or perform feature selection. Through this study, we propose a "wisdom of the crowd" approach to simultaneously predict rare cell populations and perform feature selection by integrating a pool of modern machine learning (ML) algorithms. Given that our approach integrates superior performing ML models across different normalization techniques based on entropy and rank, our method can detect diverse patterns existing across the model features. Furthermore, the method identifies a dynamic biomarker structure that divides the features into persistently selected, unselected, and fluctuating assemblies indicating the role of each biomarker in rare cell prediction, which can subsequently aid in studies of disease progression.

Cell Sorter Cleaning Practices and Their Impact on Instrument Sterility.

Cells isolated using electrostatic cell sorters are subsequently evaluated in a variety of in vitro and in vivo applications. Thus, manipulations to the cells during the pre- and post-sort processing as well as when the cells are being analyzed by and passing through the sorter fluidics has the potential to affect the experimental results. There are many variables to consider when seeking to preserve cellular integrity and function during the cell-sorting process. A previous study by the Association of Biomolecular Resource Facilities Flow Cytometry Research Group (FCRG) investigated downstream effects on different cell types as a function of sorting variables such as pressure, nozzle size, and temperature. This multisite study revealed site-to-site variability based on differential gene expression when the same cell type and sort conditions were used. These results indicated the possibility that environmental factors such as the presence of contaminants in the sorter fluidics could exhibit effects on downstream molecular assays (ie, endotoxins or RNases). In the study described here, the FCRG sought to better understand how sorters are maintained and evaluated for contaminants such as bacteria, endotoxin, and RNases. In addition, the efficacy of an endotoxin decontamination method was evaluated. The results demonstrated that the majority of sorters in shared resource laboratories are free of RNase activity and bacteria; however, many are contaminated with endotoxin. The efficacy of a hydrogen peroxide cleaning procedure was tested and found to exhibit only a short-term effectiveness in eliminating endotoxin contamination.

Identification of rare, transient post-mitotic cell states that are induced by injury and required for whole-body regeneration in Schmidtea mediterranea.

Regeneration requires the coordination of stem cells, their progeny and distant differentiated tissues. Here, we present a comprehensive atlas of whole-body regeneration in Schmidtea mediterranea and identify wound-induced cell states. An analysis of 299,998 single-cell transcriptomes captured from regeneration-competent and regeneration-incompetent fragments identified transient regeneration-activated cell states (TRACS) in the muscle, epidermis and intestine. TRACS were independent of stem cell division with distinct spatiotemporal distributions, and RNAi depletion of TRACS-enriched genes produced regeneration defects. Muscle expression of notum, follistatin, evi/wls, glypican-1 and junctophilin-1 was required for tissue polarity. Epidermal expression of agat-1/2/3, cyp3142a1, zfhx3 and atp1a1 was important for stem cell proliferation. Finally, expression of spectrinß and atp12a in intestinal basal cells, and lrrk2, cathepsinB, myosin1e, polybromo-1 and talin-1 in intestinal enterocytes regulated stem cell proliferation and tissue remodelling, respectively. Our results identify cell types and molecules that are important for regeneration, indicating that regenerative ability can emerge from coordinated transcriptional plasticity across all three germ layers.

Tumor-initiating stem cell shapes its microenvironment into an immunosuppressive barrier and pro-tumorigenic niche.

Tumor-initiating stem cells (TSCs) are critical for drug resistance and immune escape. However, the mutual regulations between TSC and tumor microenvironment (TME) remain unclear. Using DNA-label retaining, single-cell RNA sequencing (scRNA-seq), and other approaches, we investigated intestinal adenoma in response to chemoradiotherapy (CRT), thus identifying therapy-resistant TSCs (TrTSCs). We find bidirectional crosstalk between TSCs and TME using CellPhoneDB analysis. An intriguing finding is that TSCs shape TME into a landscape that favors TSCs for immunosuppression and propagation. Using adenoma-organoid co-cultures, niche-cell depletion, and lineaging tracing, we characterize a functional role of cyclooxygenase-2 (Cox-2)-dependent signaling, predominantly occurring between tumor-associated monocytes and macrophages (TAMMs) and TrTSCs. We show that TAMMs promote TrTSC proliferation through prostaglandin E2 (PGE2)-PTGER4(EP4) signaling, which enhances ß-catenin activity via AKT phosphorylation. Thus, our study shows that the bidirectional crosstalk between TrTSC and TME results in a pro-tumorigenic and immunosuppressive contexture.

Image3C, a multimodal image-based and label-independent integrative method for single-cell analysis.

Image-based cell classification has become a common tool to identify phenotypic changes in cell populations. However, this methodology is limited to organisms possessing well-characterized species-specific reagents (e.g., antibodies) that allow cell identification, clustering, and convolutional neural network (CNN) training. In the absence of such reagents, the power of image-based classification has remained mostly off-limits to many research organisms. We have developed an image-based classification methodology we named Image3C (Image-Cytometry Cell Classification) that does not require species-specific reagents nor pre-existing knowledge about the sample. Image3C combines image-based flow cytometry with an unbiased, high-throughput cell clustering pipeline and CNN integration. Image3C exploits intrinsic cellular features and non-species-specific dyes to perform de novo cell composition analysis and detect changes between different conditions. Therefore, Image3C expands the use of image-based analyses of cell population composition to research organisms in which detailed cellular phenotypes are unknown or for which species-specific reagents are not available.

Cells are the building blocks of all living organisms. They come in many types, each with a different role. Understanding the composition of cells, i.e., how many cells and which types of cells are present inside an organ can indicate what that organ does. It can also reveal how that organ changes under different conditions, like during an infection or treatment. The most powerful methods for studying cells work well for species researchers already know a lot about, such as mice, zebrafish or humans, but not for less studied animals. To change this Accorsi, Box, Peuß et al. created a new tool called Image3C to be used for studying the composition of cells in less researched organisms. Instead of using reagents that only work for specific species, the tool uses molecules that work across many species, like dyes that stain the cell nucleus. A cell-sorting machine, known as a flow cytometer, connected to a microscope then takes pictures of hundreds of stained cells each second and Image3C groups them based on their appearance, without the need for any prior knowledge about the cell types. Accorsi et al. then tested Image3C on immune system cells of zebrafish, a well-studied animal, and apple snails, an under-studied animal. For both species, the tool was able to sort cells into groups representing different parts of the immune system. Image3C speeds up the grouping process and reduces the need for user intervention and time. This lowers the risk of bias compared to manual counting of cells. It can sort cells even when the types of cells in an organism are unknown and even when specialized reagents for an organism do not exist. This means that it could characterise the cell make-up of new tissues coming from organisms never studied before. Access to this uncharted world of cells stands to reveal previously inaccessible clues about how organs behave and evolve and allow researchers to investigate the impact of environmental changes on these cells.

Identification of stem cells from large cell populations with topological scoring.

Machine learning and topological analysis methods are becoming increasingly used on various large-scale omics datasets. Modern high dimensional flow cytometry data sets share many features with other omics datasets like genomics and proteomics. For example, genomics or proteomics datasets can be sparse and have high dimensionality, and flow cytometry datasets can also share these features. This makes flow cytometry data potentially a suitable candidate for employing machine learning and topological scoring strategies, for example, to gain novel insights into patterns within the data. We have previously developed a Topological Score (TopS) and implemented it for the analysis of quantitative protein interaction network datasets. Here we show that TopS approach for large scale data analysis is applicable to the analysis of a previously described flow cytometry sorted human hematopoietic stem cell dataset. We demonstrate that TopS is capable of effectively sorting this dataset into cell populations and identify rare cell populations. We demonstrate the utility of TopS when coupled with multiple approaches including topological data analysis, X-shift clustering, and t-Distributed Stochastic Neighbor Embedding (t-SNE). Our results suggest that TopS could be effectively used to analyze large scale flow cytometry datasets to find rare cell populations.

Author Correction: Insulin resistance in cavefish as an adaptation to a nutrient-limited environment.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Evaluating the Effects of Cell Sorting on Gene Expression.

Cell sorting is a commonly used technology to isolate highly purified cell populations for downstream applications. Because the sorted cells are destined for further analysis, i.e., gene expression assays or functional assays, ensuring that the sorting process itself has little effect on the cells is of utmost importance. Previous studies examining the effects of sorting on cellular function have primarily focused on a specific cell type or condition. One of the goals of the Flow Cytometry Research Group of the Association of Biomolecular Resource Facilities is to establish best practice guidelines for cell sorting conditions that minimize cell stress, perturbation, or injury to the sorted cell population. In this study, the effects of nozzle size, sample pressure, UV exposure, and instrument type were evaluated for their effects on gene expression and cell cycle using both established cell lines and primary cells across several flow cytometry shared facilities. Results indicate that nozzle size and pressure, as well as UV exposure and instrument type, have only minor effects on gene expression, which were diminished by subsequent culturing of the sorted cells. In this assessment, these data demonstrate that cell sorting itself, regardless of instrumentation used, has minimal effects on downstream cellular applications.

Evaluating the Effects of Cell Sorting on Gene Expression.

Cell sorting is a commonly used technology to isolate highly purified cell populations for downstream applications. Because the sorted cells are destined for further analysis, i.e., gene expression assays or functional assays, ensuring that the sorting process itself has little effect on the cells is of utmost importance. Previous studies examining the effects of sorting on cellular function have primarily focused on a specific cell type or condition. One of the goals of the Flow Cytometry Research Group of the Association of Biomolecular Resource Facilities is to establish best practice guidelines for cell sorting conditions that minimize cell stress, perturbation, or injury to the sorted cell population. In this study, the effects of nozzle size, sample pressure, UV exposure, and instrument type were evaluated for their effects on gene expression and cell cycle using both established cell lines and primary cells across several flow cytometry shared facilities. Results indicate that nozzle size and pressure, as well as UV exposure and instrument type, have only minor effects on gene expression, which were diminished by subsequent culturing of the sorted cells. In this assessment, these data demonstrate that cell sorting itself, regardless of instrumentation used, has minimal effects on downstream cellular applications.

Adaptation to low parasite abundance affects immune investment and immunopathological responses of cavefish.

Reduced parasitic infection rates in the developed world are suspected to underlie the rising prevalence of autoimmune disorders. However, the long-term evolutionary consequences of decreased parasite exposure on an immune system are not well understood. We used the Mexican tetra Astyanax mexicanus to understand how loss of parasite diversity influences the evolutionary trajectory of the vertebrate immune system, by comparing river with cave morphotypes. Here, we present field data affirming a strong reduction in parasite diversity in the cave ecosystem, and show that cavefish immune cells display a more sensitive pro-inflammatory response towards bacterial endotoxins. Surprisingly, other innate cellular immune responses, such as phagocytosis, are drastically decreased in cavefish. Using two independent single-cell approaches, we identified a shift in the overall immune cell composition in cavefish as the underlying cellular mechanism, indicating strong differences in the immune investment strategy. While surface fish invest evenly into the innate and adaptive immune systems, cavefish shifted immune investment to the adaptive immune system, and here, mainly towards specific T-cell populations that promote homeostasis. Additionally, inflammatory responses and immunopathological phenotypes in visceral adipose tissue are drastically reduced in cavefish. Our data indicate that long-term adaptation to low parasite diversity coincides with a more sensitive immune system in cavefish, which is accompanied by a reduction in the immune cells that play a role in mediating the pro-inflammatory response.

Detecting and Characterizing Protein Self-Assembly In Vivo by Flow Cytometry.

Protein self-assembly governs protein function and compartmentalizes cellular processes in space and time. Current methods to study it suffer from low-sensitivity, indirect read-outs, limited throughput, and/or population-level rather than single-cell resolution. We designed a flow cytometry-based single methodology that addresses all of these limitations: Distributed Amphifluoric FRET or DAmFRET. DAmFRET detects and quantifies protein self-assemblies by sensitized emission FRET in vivo, enables deployment across model systems-from yeast to human cells-and achieves sensitive, single-cell, high-throughput read-outs irrespective of protein localization or solubility.

A Population of Navigator Neurons Is Essential for Olfactory Map Formation during the Critical Period.

In the developing brain, heightened plasticity during the critical period enables the proper formation of neural circuits. Here, we identify the "navigator" neurons, a group of perinatally born olfactory sensory neurons, as playing an essential role in establishing the olfactory map during the critical period. The navigator axons project circuitously in the olfactory bulb and traverse multiple glomeruli before terminating in perspective glomeruli. These neurons undergo a phase of exuberant axon growth and exhibit a shortened lifespan. Single-cell transcriptome analyses reveal distinct molecular signatures for the navigators. Extending their lifespan prolongs the period of exuberant growth and perturbs axon convergence. Conversely, a genetic ablation experiment indicates that, despite postnatal neurogenesis, only the navigators are endowed with the ability to establish a convergent map. The presence and the proper removal of the navigator neurons are both required to establish tight axon convergence into the glomeruli.

Quantifying Nucleation In Vivo Reveals the Physical Basis of Prion-like Phase Behavior.

Protein self-assemblies modulate protein activities over biological timescales that can exceed the lifetimes of the proteins or even the cells that harbor them. We hypothesized that these timescales relate to kinetic barriers inherent to the nucleation of ordered phases. To investigate nucleation barriers in living cells, we developed distributed amphifluoric FRET (DAmFRET). DAmFRET exploits a photoconvertible fluorophore, heterogeneous expression, and large cell numbers to quantify via flow cytometry the extent of a protein's self-assembly as a function of cellular concentration. We show that kinetic barriers limit the nucleation of ordered self-assemblies and that the persistence of the barriers with respect to concentration relates to structure. Supersaturation resulting from sequence-encoded nucleation barriers gave rise to prion behavior and enabled a prion-forming protein, Sup35 PrD, to partition into dynamic intracellular condensates or to form toxic aggregates. Our results suggest that nucleation barriers govern cytoplasmic inheritance, subcellular organization, and proteotoxicity.

Prospectively Isolated Tetraspanin+ Neoblasts Are Adult Pluripotent Stem Cells Underlying Planaria Regeneration.

Proliferating cells known as neoblasts include pluripotent stem cells (PSCs) that sustain tissue homeostasis and regeneration of lost body parts in planarians. However, the lack of markers to prospectively identify and isolate these adult PSCs has significantly hampered their characterization. We used single-cell RNA sequencing (scRNA-seq) and single-cell transplantation to address this long-standing issue. Large-scale scRNA-seq of sorted neoblasts unveiled a novel subtype of neoblast (Nb2) characterized by high levels of PIWI-1 mRNA and protein and marked by a conserved cell-surface protein-coding gene, tetraspanin 1 (tspan-1). tspan-1-positive cells survived sub-lethal irradiation, underwent clonal expansion to repopulate whole animals, and when purified with an anti-TSPAN-1 antibody, rescued the viability of lethally irradiated animals after single-cell transplantation. The first prospective isolation of an adult PSC bridges a conceptual dichotomy between functionally and molecularly defined neoblasts, shedding light on mechanisms governing in vivo pluripotency and a source of regeneration in animals. VIDEO ABSTRACT.

Retinoid-Sensitive Epigenetic Regulation of the Hoxb Cluster Maintains Normal Hematopoiesis and Inhibits Leukemogenesis.

Hox genes modulate the properties of hematopoietic stem cells (HSCs) and reacquired Hox expression in progenitors contributes to leukemogenesis. Here, our transcriptome and DNA methylome analyses revealed that Hoxb cluster and retinoid signaling genes are predominantly enriched in LT-HSCs, and this coordinate regulation of Hoxb expression is mediated by a retinoid-dependent cis-regulatory element, distal element RARE (DERARE). Deletion of the DERARE reduced Hoxb expression, resulting in changes to many downstream signaling pathways (e.g., non-canonical Wnt signaling) and loss of HSC self-renewal and reconstitution capacity. DNA methyltransferases mediate DNA methylation on the DERARE, leading to reduced Hoxb cluster expression. Acute myeloid leukemia patients with DNMT3A mutations exhibit DERARE hypomethylation, elevated HOXB expression, and adverse outcomes. CRISPR-Cas9-mediated specific DNA methylation at DERARE attenuated HOXB expression and alleviated leukemogenesis. Collectively, these findings demonstrate pivotal roles for retinoid signaling and the DERARE in maintaining HSCs and preventing leukemogenesis by coordinate regulation of Hoxb genes.

Insulin resistance in cavefish as an adaptation to a nutrient-limited environment.

Periodic food shortages are a major challenge faced by organisms in natural habitats. Cave-dwelling animals must withstand long periods of nutrient deprivation, as-in the absence of photosynthesis-caves depend on external energy sources such as seasonal floods. Here we show that cave-adapted populations of the Mexican tetra, Astyanax mexicanus, have dysregulated blood glucose homeostasis and are insulin-resistant compared to river-adapted populations. We found that multiple cave populations carry a mutation in the insulin receptor that leads to decreased insulin binding in vitro and contributes to hyperglycaemia. Hybrid fish from surface-cave crosses carrying this mutation weigh more than non-carriers, and zebrafish genetically engineered to carry the mutation have increased body weight and insulin resistance. Higher body weight may be advantageous in caves as a strategy to cope with an infrequent food supply. In humans, the identical mutation in the insulin receptor leads to a severe form of insulin resistance and reduced lifespan. However, cavefish have a similar lifespan to surface fish and do not accumulate the advanced glycation end-products in the blood that are typically associated with the progression of diabetes-associated pathologies. Our findings suggest that diminished insulin signalling is beneficial in a nutrient-limited environment and that cavefish may have acquired compensatory mechanisms that enable them to circumvent the typical negative effects associated with failure to regulate blood glucose levels.

Single-cell transcriptome analysis of avian neural crest migration reveals signatures of invasion and molecular transitions.

Neural crest cells migrate throughout the embryo, but how cells move in a directed and collective manner has remained unclear. Here, we perform the first single-cell transcriptome analysis of cranial neural crest cell migration at three progressive stages in chick and identify and establish hierarchical relationships between cell position and time-specific transcriptional signatures. We determine a novel transcriptional signature of the most invasive neural crest Trailblazer cells that is consistent during migration and enriched for approximately 900 genes. Knockdown of several Trailblazer genes shows significant but modest changes to total distance migrated. However, in vivo expression analysis by RNAscope and immunohistochemistry reveals some salt and pepper patterns that include strong individual Trailblazer gene expression in cells within other subregions of the migratory stream. These data provide new insights into the molecular diversity and dynamics within a neural crest cell migratory stream that underlie complex directed and collective cell behaviors.