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.

Endocrine-Exocrine Signaling Drives Obesity-Associated Pancreatic Ductal Adenocarcinoma.

Obesity is a major modifiable risk factor for pancreatic ductal adenocarcinoma (PDAC), yet how and when obesity contributes to PDAC progression is not well understood. Leveraging an autochthonous mouse model, we demonstrate a causal and reversible role for obesity in early PDAC progression, showing that obesity markedly enhances tumorigenesis, while genetic or dietary induction of weight loss intercepts cancer development. Molecular analyses of human and murine samples define microenvironmental consequences of obesity that foster tumorigenesis rather than new driver gene mutations, including significant pancreatic islet cell adaptation in obesity-associated tumors. Specifically, we identify aberrant beta cell expression of the peptide hormone cholecystokinin (Cck) in response to obesity and show that islet Cck promotes oncogenic Kras-driven pancreatic ductal tumorigenesis. Our studies argue that PDAC progression is driven by local obesity-associated changes in the tumor microenvironment and implicate endocrine-exocrine signaling beyond insulin in PDAC development.

Recovering Gene Interactions from Single-Cell Data Using Data Diffusion.

Single-cell RNA sequencing technologies suffer from many sources of technical noise, including under-sampling of mRNA molecules, often termed "dropout," which can severely obscure important gene-gene relationships. To address this, we developed MAGIC (Markov affinity-based graph imputation of cells), a method that shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts. We validate MAGIC on several biological systems and find it effective at recovering gene-gene relationships and additional structures. Applied to the epithilial to mesenchymal transition, MAGIC reveals a phenotypic continuum, with the majority of cells residing in intermediate states that display stem-like signatures, and infers known and previously uncharacterized regulatory interactions, demonstrating that our approach can successfully uncover regulatory relations without perturbations.

The landscape of pioneer factor activity reveals the mechanisms of chromatin reprogramming and genome activation.

Upon fertilization, embryos undergo chromatin reprogramming and genome activation; however, the mechanisms that regulate these processes are poorly understood. Here, we generated a triple mutant for Nanog, Pou5f3, and Sox19b (NPS) in zebrafish and found that NPS pioneer chromatin opening at >50% of active enhancers. NPS regulate acetylation across core histones at enhancers and promoters, and their function in gene activation can be bypassed by recruiting histone acetyltransferase to individual genes. NPS pioneer chromatin opening individually, redundantly, or additively depending on sequence context, and we show that high nucleosome occupancy facilitates NPS pioneering activity. Nucleosome position varies based on the input of different transcription factors (TFs), providing a flexible platform to modulate pioneering activity. Altogether, our results illuminate the sequence of events during genome activation and offer a conceptual framework to understand how pioneer factors interpret the genome and integrate different TF inputs across cell types and developmental transitions.

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.

Structural and developmental principles of neuropil assembly in C. elegans.

Neuropil is a fundamental form of tissue organization within the brain1, in which densely packed neurons synaptically interconnect into precise circuit architecture2,3. However, the structural and developmental principles that govern this nanoscale precision remain largely unknown4,5. Here we use an iterative data coarse-graining algorithm termed 'diffusion condensation'6 to identify nested circuit structures within the Caenorhabditis elegans neuropil, which is known as the nerve ring. We show that the nerve ring neuropil is largely organized into four strata that are composed of related behavioural circuits. The stratified architecture of the neuropil is a geometrical representation of the functional segregation of sensory information and motor outputs, with specific sensory organs and muscle quadrants mapping onto particular neuropil strata. We identify groups of neurons with unique morphologies that integrate information across strata and that create neural structures that cage the strata within the nerve ring. We use high resolution light-sheet microscopy7,8 coupled with lineage-tracing and cell-tracking algorithms9,10 to resolve the developmental sequence and reveal principles of cell position, migration and outgrowth that guide stratified neuropil organization. Our results uncover conserved structural design principles that underlie the architecture and function of the nerve ring neuropil, and reveal a temporal progression of outgrowth-based on pioneer neurons-that guides the hierarchical development of the layered neuropil. Our findings provide a systematic blueprint for using structural and developmental approaches to understand neuropil organization within the brain.

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.

Uncovering axes of variation among single-cell cancer specimens.

While several tools have been developed to map axes of variation among individual cells, no analogous approaches exist for identifying axes of variation among multicellular biospecimens profiled at single-cell resolution. For this purpose, we developed 'phenotypic earth mover's distance' (PhEMD). PhEMD is a general method for embedding a 'manifold of manifolds', in which each datapoint in the higher-level manifold (of biospecimens) represents a collection of points that span a lower-level manifold (of cells). We apply PhEMD to a newly generated drug-screen dataset and demonstrate that PhEMD uncovers axes of cell subpopulational variation among a large set of perturbation conditions. Moreover, we show that PhEMD can be used to infer the phenotypes of biospecimens not directly profiled. Applied to clinical datasets, PhEMD generates a map of the patient-state space that highlights sources of patient-to-patient variation. PhEMD is scalable, compatible with leading batch-effect correction techniques and generalizable to multiple experimental designs.

Exploring single-cell data with deep multitasking neural networks.

It is currently challenging to analyze single-cell data consisting of many cells and samples, and to address variations arising from batch effects and different sample preparations. For this purpose, we present SAUCIE, a deep neural network that combines parallelization and scalability offered by neural networks, with the deep representation of data that can be learned by them to perform many single-cell data analysis tasks. Our regularizations (penalties) render features learned in hidden layers of the neural network interpretable. On large, multi-patient datasets, SAUCIE's various hidden layers contain denoised and batch-corrected data, a low-dimensional visualization and unsupervised clustering, as well as other information that can be used to explore the data. We analyze a 180-sample dataset consisting of 11 million T cells from dengue patients in India, measured with mass cytometry. SAUCIE can batch correct and identify cluster-based signatures of acute dengue infection and create a patient manifold, stratifying immune response to dengue.

IL-7 receptor alpha defines heterogeneity and signature of human effector memory CD8+ T cells in high dimensional analysis.

The IL-7 receptor alpha chain (IL-7Rα or CD127) can be differentially expressed in memory CD8+ T cells. Here we investigated whether IL-7Rα could serve as a key molecule in defining a comprehensive landscape of heterogeneity in human effector memory (EM) CD8+ T cells using high-dimensional Cytometry by Time-Of-Flight (CyTOF) and single-cell RNA-seq (scRNA-seq). IL-7Rα had diverse, but organized, expressional relationship in EM CD8+ T cells with molecules related to cell function and gene regulation, which rendered an immune landscape defining heterogeneous cell subsets. The differential expression of these molecules likely has biological implications as we found in vivo signatures of transcription factors and homeostasis cytokine receptors, including T-bet and IL-7Rα. Our findings indicate the existence of heterogeneity in human EM CD8+ T cells as defined by distinct but organized expression patterns of multiple molecules in relationship to IL-7Rα and its possible biological significance in modulating downstream events.

Identification and Analysis of Islet Antigen-Specific CD8+ T Cells with T Cell Libraries.

Type 1 diabetes (T1D) is most likely caused by killing of ß cells by autoreactive CD8+ T cells. Methods to isolate and identify these cells are limited by their low frequency in the peripheral blood. We analyzed CD8+ T cells, reactive with diabetes Ags, with T cell libraries and further characterized their phenotype by CyTOF using class I MHC tetramers. In the libraries, the frequency of islet Ag-specific CD45RO+IFN-γ+CD8+ T cells was higher in patients with T1D compared with healthy control subjects. Ag-specific cells from the libraries of patients with T1D were reactive with ZnT8186-194, whereas those from healthy control recognized ZnT8186-194 and other Ags. ZnT8186-194-reactive CD8+ cells expressed an activation phenotype in T1D patients. We found TCR sequences that were used in multiple library wells from patients with T1D, but these sequences were private and not shared between individuals. These sequences could identify the Ag-specific T cells on a repeated draw, ex vivo in the IFN-γ+ CD8+ T cell subset. We conclude that CD8+ T cell libraries can identify Ag-specific T cells in patients with T1D. The T cell clonotypes can be tracked in vivo with identification of the TCR gene sequences.

Dissecting alterations in human CD8+ T cells with aging by high-dimensional single cell mass cytometry.

We investigated the effect of aging on the multi-dimensional characteristics and heterogeneity of human peripheral CD8+ T cells defined by the expression of a set of molecules at the single cell level using the recently developed mass cytometry or Cytometry by Time-Of-Flight (CyTOF) and computational algorithms. CD8+ T cells of young and older adults had differential expression of molecules, especially those related to cell activation and migration, permitting the clustering of young and older adults through an unbiased approach. The changes in the expression of individual molecules were collectively reflected in the altered high-dimensional profiles of CD8+ T cells in older adults as visualized by the dimensionality reduction analysis tools principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). A combination of PhenoGraph clustering and t-SNE analysis revealed heterogeneous subsets of CD8+ T cells that altered with aging. Furthermore, intermolecular quantitative relationships in CD8+ T cells appeared to change with age as determined by the computational algorithm conditional-Density Resampled Estimate of Mutual Information (DREMI). The results of our study showed that heterogeneity, multidimensional characteristics, and intermolecular quantitative relationships in human CD8+ T cells altered with age, distinctively clustering young and older adults through an unbiased approach.

Single-cell mass cytometry of TCR signaling: amplification of small initial differences results in low ERK activation in NOD mice.

Signaling from the T-cell receptor (TCR) conditions T-cell differentiation and activation, requiring exquisite sensitivity and discrimination. Using mass cytometry, a high-dimensional technique that can probe multiple signaling nodes at the single-cell level, we interrogate TCR signaling dynamics in control C57BL/6 and autoimmunity-prone nonobese diabetic (NOD) mice, which show ineffective ERK activation after TCR triggering. By quantitating signals at multiple steps along the signaling cascade and parsing the phosphorylation level of each node as a function of its predecessors, we show that a small impairment in initial pCD3ζ activation resonates farther down the signaling cascade and results in larger defects in activation of the ERK1/2-S6 and IκBα modules. This nonlinear property of TCR signaling networks, which magnifies small initial differences during signal propagation, also applies in cells from B6 mice activated at different levels of intensity. Impairment in pCD3ζ and pSLP76 is not a feedback consequence of a primary deficiency in ERK activation because no proximal signaling defect was observed in Erk2 KO T cells. These defects, which were manifest at all stages of T-cell differentiation from early thymic pre-T cells to memory T cells, may condition the imbalanced immunoregulation and tolerance in NOD T cells. More generally, this amplification of small initial differences in signal intensity may explain how T cells discriminate between closely related ligands and adopt strongly delineated cell fates.

Novel cell states arise in embryonic cells devoid of key reprogramming factors.

The capacity for embryonic cells to differentiate relies on a large-scale reprogramming of the oocyte and sperm nucleus into a transient totipotent state. In zebrafish, this reprogramming step is achieved by the pioneer factors Nanog, Pou5f3, and Sox19b (NPS). Yet, it remains unclear whether cells lacking this reprogramming step are directed towards wild type states or towards novel developmental canals in the Waddington landscape of embryonic development. Here we investigate the developmental fate of embryonic cells mutant for NPS by analyzing their single-cell gene expression profiles. We find that cells lacking the first developmental reprogramming steps can acquire distinct cell states. These states are manifested by gene expression modules that result from a failure of nuclear reprogramming, the persistence of the maternal program, and the activation of somatic compensatory programs. As a result, most mutant cells follow new developmental canals and acquire new mixed cell states in development. In contrast, a group of mutant cells acquire primordial germ cell-like states, suggesting that NPS-dependent reprogramming is dispensable for these cell states. Together, these results demonstrate that developmental reprogramming after fertilization is required to differentiate most canonical developmental programs, and loss of the transient totipotent state canalizes embryonic cells into new developmental states in vivo.

Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer's disease progression.

Late onset Alzheimer's disease (AD) is a progressive neurodegenerative disease, with brain changes beginning years before symptoms surface. AD is characterized by neuronal loss, the classic feature of the disease that underlies brain atrophy. However, GWAS reports and recent single-nucleus RNA sequencing (snRNA-seq) efforts have highlighted that glial cells, particularly microglia, claim a central role in AD pathophysiology. Here, we tailor pattern-learning algorithms to explore distinct gene programs by integrating the entire transcriptome, yielding distributed AD-predictive modules within the brain's major cell-types. We show that these learned modules are biologically meaningful through the identification of new and relevant enriched signaling cascades. The predictive nature of our modules, especially in microglia, allows us to infer each subject's progression along a disease pseudo-trajectory, confirmed by post-mortem pathological brain tissue markers. Additionally, we quantify the interplay between pairs of cell-type modules in the AD brain, and localized known AD risk genes to enriched module gene programs. Our collective findings advocate for a transition from cell-type-specificity to gene modules specificity to unlock the potential of unique gene programs, recasting the roles of recently reported genome-wide AD risk loci.

StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records.

Determining acute ischemic stroke (AIS) etiology is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification tool, StrokeClassifier, using electronic health record (EHR) text from 2039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology adjudicated by agreement of at least 2 board-certified vascular neurologists' review of the 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 vascular neurologists' diagnoses, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 and weighted F1 of 0.74 for multi-class classification. In MIMIC-III, its accuracy and weighted F1 were 0.70 and 0.71, respectively. In binary classification, the two metrics ranged from 0.77 to 0.96. 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 designed a certainty heuristic to grade the confidence of StrokeClassifier's diagnosis as non-cryptogenic by the degree of consensus among the 9 classifiers and applied it to 788 cryptogenic patients, reducing cryptogenic diagnoses from 25.2% to 7.2%. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.

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.

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.

Normalization of mass cytometry data with bead standards.

Mass cytometry uses atomic mass spectrometry combined with isotopically pure reporter elements to currently measure as many as 40 parameters per single cell. As with any quantitative technology, there is a fundamental need for quality assurance and normalization protocols. In the case of mass cytometry, the signal variation over time due to changes in instrument performance combined with intervals between scheduled maintenance must be accounted for and then normalized. Here, samples were mixed with polystyrene beads embedded with metal lanthanides, allowing monitoring of mass cytometry instrument performance over multiple days of data acquisition. The protocol described here includes simultaneous measurements of beads and cells on the mass cytometer, subsequent extraction of the bead-based signature, and the application of an algorithm enabling correction of both short- and long-term signal fluctuations. The variation in the intensity of the beads that remains after normalization may also be used to determine data quality. Application of the algorithm to a one-month longitudinal analysis of a human peripheral blood sample reduced the range of median signal fluctuation from 4.9-fold to 1.3-fold.