Parasympathetic neurons derived from human pluripotent stem cells model human diseases and development.

Autonomic parasympathetic neurons (parasymNs) control unconscious body responses, including "rest-and-digest." ParasymN innervation is important for organ development, and parasymN dysfunction is a hallmark of autonomic neuropathy. However, parasymN function and dysfunction in humans are vastly understudied due to the lack of a model system. Human pluripotent stem cell (hPSC)-derived neurons can fill this void as a versatile platform. Here, we developed a differentiation paradigm detailing the derivation of functional human parasymNs from Schwann cell progenitors. We employ these neurons (1) to assess human autonomic nervous system (ANS) development, (2) to model neuropathy in the genetic disorder familial dysautonomia (FD), (3) to show parasymN dysfunction during SARS-CoV-2 infection, (4) to model the autoimmune disease Sjögren's syndrome (SS), and (5) to show that parasymNs innervate white adipocytes (WATs) during development and promote WAT maturation. Our model system could become instrumental for future disease modeling and drug discovery studies, as well as for human developmental studies.

Defining and benchmarking open problems in single-cell analysis.

With the growing number of single-cell analysis tools, benchmarks are increasingly important to guide analysis and method development. However, a lack of standardisation and extensibility in current benchmarks limits their usability, longevity, and relevance to the community. We present Open Problems, a living, extensible, community-guided benchmarking platform including 10 current single-cell tasks that we envision will raise standards for the selection, evaluation, and development of methods in single-cell analysis.

Stem cell migration drives lung repair in living mice.

Tissue repair requires a highly coordinated cellular response to injury. In the lung, alveolar type 2 cells (AT2s) act as stem cells to replenish both themselves and alveolar type 1 cells (AT1s); however, the complex orchestration of stem cell activity after injury is poorly understood. Here, we establish longitudinal imaging of AT2s in murine intact tissues ex vivo and in vivo in order to track their dynamic behavior over time. We discover that a large fraction of AT2s become motile following injury and provide direct evidence for their migration between alveolar units. High-resolution morphokinetic mapping of AT2s further uncovers the emergence of distinct motile phenotypes. Inhibition of AT2 migration via genetic depletion of ArpC3 leads to impaired regeneration of AT2s and AT1s in vivo. Together, our results establish a requirement for stem cell migration between alveolar units and identify properties of stem cell motility at high cellular resolution.

Trellis tree-based analysis reveals stromal regulation of patient-derived organoid drug responses.

Patient-derived organoids (PDOs) can model personalized therapy responses; however, current screening technologies cannot reveal drug response mechanisms or how tumor microenvironment cells alter therapeutic performance. To address this, we developed a highly multiplexed mass cytometry platform to measure post-translational modification (PTM) signaling, DNA damage, cell-cycle activity, and apoptosis in >2,500 colorectal cancer (CRC) PDOs and cancer-associated fibroblasts (CAFs) in response to clinical therapies at single-cell resolution. To compare patient- and microenvironment-specific drug responses in thousands of single-cell datasets, we developed "Trellis"-a highly scalable, tree-based treatment effect analysis method. Trellis single-cell screening revealed that on-target cell-cycle blockage and DNA-damage drug effects are common, even in chemorefractory PDOs. However, drug-induced apoptosis is rarer, patient-specific, and aligns with cancer cell PTM signaling. We find that CAFs can regulate PDO plasticity-shifting proliferative colonic stem cells (proCSCs) to slow-cycling revival colonic stem cells (revCSCs) to protect cancer cells from chemotherapy.

Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning.

The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.

StrokeClassifier: Ischemic Stroke Etiology Classification by Ensemble Consensus Modeling Using Electronic Health Records.

Determining the etiology of an acute ischemic stroke (AIS) is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification machine intelligence tool, StrokeClassifier, using electronic health record (EHR) text data from 2,039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology determined by agreement of at least 2 board-certified vascular neurologists' review of the stroke hospitalization EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with stroke etiologies adjudicated by vascular neurologists, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 (±0.01) and weighted F1 of 0.74 (±0.01). In the MIMIC-III cohort, the accuracy and weighted F1 of StrokeClassifier were 0.70 and 0.71, respectively. SHapley Additive exPlanation analysis elucidated that the top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We then designed a certainty heuristic to deem a StrokeClassifier diagnosis as confidently non-cryptogenic by the degree of consensus among the 9 classifiers, and applied it to 788 cryptogenic patients. This reduced the percentage of the cryptogenic strokes from 25.2% to 7.2% of all ischemic strokes. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology for individual patients. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.

GEODESIC SINKHORN FOR FAST AND ACCURATE OPTIMAL TRANSPORT ON MANIFOLDS.

Efficient computation of optimal transport distance between distributions is of growing importance in data science. Sinkhorn-based methods are currently the state-of-the-art for such computations, but require On2 computations. In addition, Sinkhorn-based methods commonly use an Euclidean ground distance between datapoints. However, with the prevalence of manifold structured scientific data, it is often desirable to consider geodesic ground distance. Here, we tackle both issues by proposing Geodesic Sinkhorn-based on diffusing a heat kernel on a manifold graph. Notably, Geodesic Sinkhorn requires only O(nlog⁡n) computation, as we approximate the heat kernel with Chebyshev polynomials based on the sparse graph Laplacian. We apply our method to the computation of barycenters of several distributions of high dimensional single cell data from patient samples undergoing chemotherapy. In particular, we define the barycentric distance as the distance between two such barycenters. Using this definition, we identify an optimal transport distance and path associated with the effect of treatment on cellular data.

Multi-view manifold learning of human brain-state trajectories.

The complexity of the human brain gives the illusion that brain activity is intrinsically high-dimensional. Nonlinear dimensionality-reduction methods such as uniform manifold approximation and t-distributed stochastic neighbor embedding have been used for high-throughput biomedical data. However, they have not been used extensively for brain activity data such as those from functional magnetic resonance imaging (fMRI), primarily due to their inability to maintain dynamic structure. Here we introduce a nonlinear manifold learning method for time-series data-including those from fMRI-called temporal potential of heat-diffusion for affinity-based transition embedding (T-PHATE). In addition to recovering a low-dimensional intrinsic manifold geometry from time-series data, T-PHATE exploits the data's autocorrelative structure to faithfully denoise and unveil dynamic trajectories. We empirically validate T-PHATE on three fMRI datasets, showing that it greatly improves data visualization, classification, and segmentation of the data relative to several other state-of-the-art dimensionality-reduction benchmarks. These improvements suggest many potential applications of T-PHATE to other high-dimensional datasets of temporally diffuse processes.

A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction.

Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensional, high throughput, noisy datasets. Such datasets are especially present in fields like biology and physics. While it is thought that these methods preserve underlying manifold structure of data by learning a proxy for geodesic distances, no specific theoretical links have been established. Here, we establish such a link via results in Riemannian geometry explicitly connecting heat diffusion to manifold distances. In this process, we also formulate a more general heat kernel based manifold embedding method that we call heat geodesic embeddings. This novel perspective makes clearer the choices available in manifold learning and denoising. Results show that our method outperforms existing state of the art in preserving ground truth manifold distances, and preserving cluster structure in toy datasets. We also showcase our method on single cell RNA-sequencing datasets with both continuum and cluster structure, where our method enables interpolation of withheld timepoints of data. Finally, we show that parameters of our more general method can be configured to give results similar to PHATE (a state-of-the-art diffusion based manifold learning method) as well as SNE (an attraction/repulsion neighborhood based method that forms the basis of t-SNE).

Multiscale geometric and topological analyses for characterizing and predicting immune responses from single cell data.

Single cell genomics has revolutionized our ability to map immune heterogeneity and responses. With the influx of large-scale data sets from diverse modalities, the resolution achieved has supported the long-held notion that immune cells are naturally organized into hierarchical relationships, characterized at multiple levels. Such a multigranular structure corresponds to key geometric and topological features. Given that differences between an effective and ineffective immunological response may not be found at one level, there is vested interest in characterizing and predicting outcomes from such features. In this review, we highlight single cell methods and principles for learning geometric and topological properties of data at multiple scales, discussing their contributions to immunology. Ultimately, multiscale approaches go beyond classical clustering, revealing a more comprehensive picture of cellular heterogeneity.

PD-1 maintains CD8 T cell tolerance towards cutaneous neoantigens.

The peripheral T cell repertoire of healthy individuals contains self-reactive T cells1,2. Checkpoint receptors such as PD-1 are thought to enable the induction of peripheral tolerance by deletion or anergy of self-reactive CD8 T cells3-10. However, this model is challenged by the high frequency of immune-related adverse events in patients with cancer who have been treated with checkpoint inhibitors11. Here we developed a mouse model in which skin-specific expression of T cell antigens in the epidermis caused local infiltration of antigen-specific CD8 T cells with an effector gene-expression profile. In this setting, PD-1 enabled the maintenance of skin tolerance by preventing tissue-infiltrating antigen-specific effector CD8 T cells from (1) acquiring a fully functional, pathogenic differentiation state, (2) secreting significant amounts of effector molecules, and (3) gaining access to epidermal antigen-expressing cells. In the absence of PD-1, epidermal antigen-expressing cells were eliminated by antigen-specific CD8 T cells, resulting in local pathology. Transcriptomic analysis of skin biopsies from two patients with cutaneous lichenoid immune-related adverse events showed the presence of clonally expanded effector CD8 T cells in both lesional and non-lesional skin. Thus, our data support a model of peripheral T cell tolerance in which PD-1 allows antigen-specific effector CD8 T cells to co-exist with antigen-expressing cells in tissues without immunopathology.

Single-cell analysis reveals inflammatory interactions driving macular degeneration.

Due to commonalities in pathophysiology, age-related macular degeneration (AMD) represents a uniquely accessible model to investigate therapies for neurodegenerative diseases, leading us to examine whether pathways of disease progression are shared across neurodegenerative conditions. Here we use single-nucleus RNA sequencing to profile lesions from 11 postmortem human retinas with age-related macular degeneration and 6 control retinas with no history of retinal disease. We create a machine-learning pipeline based on recent advances in data geometry and topology and identify activated glial populations enriched in the early phase of disease. Examining single-cell data from Alzheimer's disease and progressive multiple sclerosis with our pipeline, we find a similar glial activation profile enriched in the early phase of these neurodegenerative diseases. In late-stage age-related macular degeneration, we identify a microglia-to-astrocyte signaling axis mediated by interleukin-1ß which drives angiogenesis characteristic of disease pathogenesis. We validated this mechanism using in vitro and in vivo assays in mouse, identifying a possible new therapeutic target for AMD and possibly other neurodegenerative conditions. Thus, due to shared glial states, the retina provides a potential system for investigating therapeutic approaches in neurodegenerative diseases.

linc-mipep and linc-wrb encode micropeptides that regulate chromatin accessibility in vertebrate-specific neural cells.

Thousands of long intergenic non-coding RNAs (lincRNAs) are transcribed throughout the vertebrate genome. A subset of lincRNAs enriched in developing brains have recently been found to contain cryptic open-reading frames and are speculated to encode micropeptides. However, systematic identification and functional assessment of these transcripts have been hindered by technical challenges caused by their small size. Here, we show that two putative lincRNAs (linc-mipep, also called lnc-rps25, and linc-wrb) encode micropeptides with homology to the vertebrate-specific chromatin architectural protein, Hmgn1, and demonstrate that they are required for development of vertebrate-specific brain cell types. Specifically, we show that NMDA receptor-mediated pathways are dysregulated in zebrafish lacking these micropeptides and that their loss preferentially alters the gene regulatory networks that establish cerebellar cells and oligodendrocytes - evolutionarily newer cell types that develop postnatally in humans. These findings reveal a key missing link in the evolution of vertebrate brain cell development and illustrate a genetic basis for how some neural cell types are more susceptible to chromatin disruptions, with implications for neurodevelopmental disorders and disease.

HSV-2 triggers upregulation of MALAT1 in CD4+ T cells and promotes HIV latency reversal.

Herpes simplex virus type 2 (HSV-2) coinfection is associated with increased HIV-1 viral loads and expanded tissue reservoirs, but the mechanisms are not well defined. HSV-2 recurrences result in an influx of activated CD4+ T cells to sites of viral replication and an increase in activated CD4+ T cells in peripheral blood. We hypothesized that HSV-2 induces changes in these cells that facilitate HIV-1 reactivation and replication and tested this hypothesis in human CD4+ T cells and 2D10 cells, a model of HIV-1 latency. HSV-2 promoted latency reversal in HSV-2-infected and bystander 2D10 cells. Bulk and single-cell RNA-Seq studies of activated primary human CD4+ T cells identified decreased expression of HIV-1 restriction factors and increased expression of transcripts including MALAT1 that could drive HIV replication in both the HSV-2-infected and bystander cells. Transfection of 2D10 cells with VP16, an HSV-2 protein that regulates transcription, significantly upregulated MALAT1 expression, decreased trimethylation of lysine 27 on histone H3 protein, and triggered HIV latency reversal. Knockout of MALAT1 from 2D10 cells abrogated the response to VP16 and reduced the response to HSV-2 infection. These results demonstrate that HSV-2 contributes to HIV-1 reactivation through diverse mechanisms, including upregulation of MALAT1 to release epigenetic silencing.

Cell cycle controls long-range calcium signaling in the regenerating epidermis.

Skin homeostasis is maintained by stem cells, which must communicate to balance their regenerative behaviors. Yet, how adult stem cells signal across regenerative tissue remains unknown due to challenges in studying signaling dynamics in live mice. We combined live imaging in the mouse basal stem cell layer with machine learning tools to analyze patterns of Ca2+ signaling. We show that basal cells display dynamic intercellular Ca2+ signaling among local neighborhoods. We find that these Ca2+ signals are coordinated across thousands of cells and that this coordination is an emergent property of the stem cell layer. We demonstrate that G2 cells are required to initiate normal levels of Ca2+ signaling, while connexin43 connects basal cells to orchestrate tissue-wide coordination of Ca2+ signaling. Lastly, we find that Ca2+ signaling drives cell cycle progression, revealing a communication feedback loop. This work provides resolution into how stem cells at different cell cycle stages coordinate tissue-wide signaling during epidermal regeneration.

Integrated transcriptome and trajectory analysis of cutaneous T-cell lymphoma identifies putative precancer populations.

The incidence of cutaneous T-cell lymphoma (CTCL) increases with age, and blood involvement portends a worse prognosis. To advance our understanding of the development of CTCL and identify potential therapeutic targets, we performed integrative analyses of paired single-cell RNA and T-cell receptor (TCR) sequencing of peripheral blood CD4+ T cells from patients with CTCL to reveal disease-unifying features. The malignant CD4+ T cells of CTCL showed highly diverse transcriptomic profiles across patients, with most displaying a mature Th2 differentiation and T-cell exhaustion phenotype. TCR-CDR3 peptide prediction analysis suggested limited diversity between CTCL samples, consistent with a role for a common antigenic stimulus. Potential of heat diffusion for affinity-based trajectory embedding transition analysis identified putative precancerous circulating populations characterized by an intermediate stage of gene expression and mutation level between the normal CD4+ T cells and malignant CTCL cells. We further revealed the therapeutic potential of targeting CD82 and JAK that endow the malignant CTCL cells with survival and proliferation advantages.

Single-cell multi-modal GAN reveals spatial patterns in single-cell data from triple-negative breast cancer.

Exciting advances in technologies to measure biological systems are currently at the forefront of research. The ability to gather data along an increasing number of omic dimensions has created a need for tools to analyze all of this information together, rather than siloing each technology into separate analysis pipelines. To advance this goal, we introduce a framework called the single-cell multi-modal generative adversarial network (scMMGAN) that integrates data from multiple modalities into a unified representation in the ambient data space for downstream analysis using a combination of adversarial learning and data geometry techniques. The framework's key improvement is an additional diffusion geometry loss with a new kernel that constrains the otherwise over-parameterized GAN. We demonstrate scMMGAN's ability to produce more meaningful alignments than alternative methods on a wide variety of data modalities and that its output can be used to draw conclusions from real-world biological experimental data.

Deep learning for quality assessment of optical coherence tomography angiography images.

Optical coherence tomography angiography (OCTA) is an emerging non-invasive technique for imaging the retinal vasculature. While there are many promising clinical applications for OCTA, determination of image quality remains a challenge. We developed a deep learning-based system using a ResNet152 neural network classifier, pretrained using ImageNet, to classify images of the superficial capillary plexus in 347 scans from 134 patients. Images were also manually graded by two independent graders as a ground truth for the supervised learning models. Because requirements for image quality may vary depending on the clinical or research setting, two models were trained-one to identify high-quality images and one to identify low-quality images. Our neural network models demonstrated outstanding area under the curve (AUC) metrics for both low quality image identification (AUC = 0.99, 95%CI 0.98-1.00, [Formula: see text] = 0.90) and high quality image identification (AUC = 0.97, 95%CI 0.96-0.99, [Formula: see text] = 0.81), significantly outperforming machine-reported signal strength (AUC = 0.82, 95%CI 0.77-0.86, [Formula: see text]= 0.52 and AUC = 0.78, 95%CI 0.73-0.83, [Formula: see text] = 0.27 respectively). Our study demonstrates that techniques from machine learning may be used to develop flexible and robust methods for quality control of OCTA images.

Immune cells and their inflammatory mediators modify ß cells and cause checkpoint inhibitor-induced diabetes.

Checkpoint inhibitors (CPIs) targeting programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) and cytotoxic T lymphocyte antigen 4 (CTLA-4) have revolutionized cancer treatment but can trigger autoimmune complications, including CPI-induced diabetes mellitus (CPI-DM), which occurs preferentially with PD-1 blockade. We found evidence of pancreatic inflammation in patients with CPI-DM with shrinkage of pancreases, increased pancreatic enzymes, and in a case from a patient who died with CPI-DM, peri-islet lymphocytic infiltration. In the NOD mouse model, anti-PD-L1 but not anti-CTLA-4 induced diabetes rapidly. RNA sequencing revealed that cytolytic IFN-γ+CD8+ T cells infiltrated islets with anti-PD-L1. Changes in ß cells were predominantly driven by IFN-γ and TNF-α and included induction of a potentially novel ß cell population with transcriptional changes suggesting dedifferentiation. IFN-γ increased checkpoint ligand expression and activated apoptosis pathways in human ß cells in vitro. Treatment with anti-IFN-γ and anti-TNF-α prevented CPI-DM in anti-PD-L1-treated NOD mice. CPIs targeting the PD-1/PD-L1 pathway resulted in transcriptional changes in ß cells and immune infiltrates that may lead to the development of diabetes. Inhibition of inflammatory cytokines can prevent CPI-DM, suggesting a strategy for clinical application to prevent this complication.