Earth Mover's Distance (EMD): A True Metric for Comparing Biomarker Expression Levels in Cell Populations

Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations

Intratumor heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression. There is a pressing need to understand the functions of heterogeneous tumor cell subpopulations within a tumor, yet biological systems to study these processes in vitro are limited. With the advent of single-cell RNA sequencing (scRNA-seq), it has become clear that some cancer cell line models include distinct subpopulations. Heterogeneous cell lines offer a unique opportunity to study the dynamics and evolution of genetically similar cancer cell subpopulations in controlled experimental settings. Here, we present clusterCleaver, a computational package that uses metrics of statistical distance to identify candidate surface markers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation. clusterCleaver was experimentally validated using the MDA-MB-231 and MDA-MB-436 breast cancer cell lines. ESAM and BST2/tetherin were experimentally confirmed as surface markers which identify and separate major transcriptomic subpopulations within MDA-MB-231 and MDA-MB-436 cells, respectively. clusterCleaver is a computationally efficient and experimentally validated workflow for identification and enrichment of distinct subpopulations within cell lines which paves the way for studies on the coexistence of cancer cell subpopulations in well-defined in vitro systems.

Evolutionary and developmental specialization of foveal cell types in the marmoset

In primates, high-acuity vision is mediated by the fovea, a small specialized central region of the retina. The fovea, unique to the anthropoid lineage among mammals, undergoes notable neuronal morphological changes during postnatal maturation. However, the extent of cellular similarity across anthropoid foveas and the molecular underpinnings of foveal maturation remain unclear. Here, we used high-throughput single-cell RNA sequencing to profile retinal cells of the common marmoset (Callithrix jacchus), an early divergent in anthropoid evolution from humans, apes, and macaques. We generated atlases of the marmoset fovea and peripheral retina for both neonates and adults. Our comparative analysis revealed that marmosets share almost all their foveal types with both humans and macaques, highlighting a conserved cellular structure among primate foveas. Furthermore, by tracing the developmental trajectory of cell types in the foveal and peripheral retina, we found distinct maturation paths for each. In-depth analysis of gene expression differences demonstrated that cone photoreceptors and Müller glia (MG), among others, show the greatest molecular divergence between these two regions. Utilizing single-cell ATAC-seq and gene-regulatory network inference, we uncovered distinct transcriptional regulations differentiating foveal cones from their peripheral counterparts. Further analysis of predicted ligand-receptor interactions suggested a potential role for MG in supporting the maturation of foveal cones. Together, these results provide valuable insights into foveal development, structure, and evolution.

Gene trajectory inference for single-cell data by optimal transport metrics

Single-cell RNA sequencing has been widely used to investigate cell state transitions and gene dynamics of biological processes. Current strategies to infer the sequential dynamics of genes in a process typically rely on constructing cell pseudotime through cell trajectory inference. However, the presence of concurrent gene processes in the same group of cells and technical noise can obscure the true progression of the processes studied. To address this challenge, we present GeneTrajectory, an approach that identifies trajectories of genes rather than trajectories of cells. Specifically, optimal transport distances are calculated between gene distributions across the cell-cell graph to extract gene programs and define their gene pseudotemporal order. Here we demonstrate that GeneTrajectory accurately extracts progressive gene dynamics in myeloid lineage maturation. Moreover, we show that GeneTrajectory deconvolves key gene programs underlying mouse skin hair follicle dermal condensate differentiation that could not be resolved by cell trajectory approaches. GeneTrajectory facilitates the discovery of gene programs that control the changes and activities of biological processes.

Longitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1

Neurodegeneration is a protracted process involving progressive changes in myriad cell types that ultimately results in the death of vulnerable neuronal populations. To dissect how individual cell types within a heterogeneous tissue contribute to the pathogenesis and progression of a neurodegenerative disorder, we performed longitudinal single-nucleus RNA sequencing of mouse and human spinocerebellar ataxia type 1 (SCA1) cerebellar tissue, establishing continuous dynamic trajectories of each cell population. Importantly, we defined the precise transcriptional changes that precede loss of Purkinje cells and, for the first time, identified robust early transcriptional dysregulation in unipolar brush cells and oligodendroglia. Finally, we applied a deep learning method to predict disease state accurately and identified specific features that enable accurate distinction of wild-type and SCA1 cells. Together, this work reveals new roles for diverse cerebellar cell types in SCA1 and provides a generalizable analysis framework for studying neurodegeneration.

Evolutionary and Developmental Specialization of Foveal Cell Types in the Marmoset

In primates, high-acuity vision is mediated by the fovea, a small specialized central region of the retina. The fovea, unique to the anthropoid lineage among mammals, undergoes notable neuronal morphological changes during postnatal maturation. However, the extent of cellular similarity across anthropoid foveas and the molecular underpinnings of foveal maturation remain unclear. Here, we used high throughput single cell RNA sequencing to profile retinal cells of the common marmoset ( Callithrix jacchus ), an early divergent in anthropoid evolution from humans, apes, and macaques. We generated atlases of the marmoset fovea and peripheral retina for both neonates and adults. Our comparative analysis revealed that marmosets share almost all its foveal types with both humans and macaques, highlighting a conserved cellular structure among primate foveas. Furthermore, by tracing the developmental trajectory of cell types in the foveal and peripheral retina, we found distinct maturation paths for each. In-depth analysis of gene expression differences demonstrated that cone photoreceptors and Müller glia, among others, show the greatest molecular divergence between these two regions. Utilizing single-cell ATAC-seq and gene-regulatory network inference, we uncovered distinct transcriptional regulations differentiating foveal cones from their peripheral counterparts. Further analysis of predicted ligand-receptor interactions suggested a potential role for Müller glia in supporting the maturation of foveal cones. Together, these results provide valuable insights into foveal development, structure, and evolution.

Significance statement

The sharpness of our eyesight hinges on a tiny retinal region known as the fovea. The fovea is pivotal for primate vision and is susceptible to diseases like age-related macular degeneration. We studied the fovea in the marmoset-a primate with ancient evolutionary ties. Our data illustrated the cellular and molecular composition of its fovea across different developmental ages. Our findings highlighted a profound cellular consistency among marmosets, humans, and macaques, emphasizing the value of marmosets in visual research and the study of visual diseases.

An oncogenic phenoscape of colonic stem cell polarization

Cancer cells are regulated by oncogenic mutations and microenvironmental signals, yet these processes are often studied separately. To functionally map how cell-intrinsic and cell-extrinsic cues co-regulate cell fate, we performed a systematic single-cell analysis of 1,107 colonic organoid cultures regulated by (1) colorectal cancer (CRC) oncogenic mutations, (2) microenvironmental fibroblasts and macrophages, (3) stromal ligands, and (4) signaling inhibitors. Multiplexed single-cell analysis revealed a stepwise epithelial differentiation phenoscape dictated by combinations of oncogenes and stromal ligands, spanning from fibroblast-induced Clusterin (CLU)+ revival colonic stem cells (revCSCs) to oncogene-driven LRIG1+ hyper-proliferative CSCs (proCSCs). The transition from revCSCs to proCSCs is regulated by decreasing WNT3A and TGF-β-driven YAP signaling and increasing KRASG12D or stromal EGF/Epiregulin-activated MAPK/PI3K flux. We find that APC loss and KRASG12D collaboratively limit access to revCSCs and disrupt stromal-epithelial communication-trapping epithelia in the proCSC fate. These results reveal that oncogenic mutations dominate homeostatic differentiation by obstructing cell-extrinsic regulation of cell-fate plasticity.

Speech Reception in Young Children with Autism Is Selectively Indexed by a Neural Oscillation Coupling Anomaly

Communication difficulties are one of the core criteria in diagnosing autism spectrum disorder (ASD), and are often characterized by speech reception difficulties, whose biological underpinnings are not yet identified. This deficit could denote atypical neuronal ensemble activity, as reflected by neural oscillations. Atypical cross-frequency oscillation coupling, in particular, could disrupt the joint tracking and prediction of dynamic acoustic stimuli, a dual process that is essential for speech comprehension. Whether such oscillatory anomalies already exist in very young children with ASD, and with what specificity they relate to individual language reception capacity is unknown. We collected neural activity data using electroencephalography (EEG) in 64 very young children with and without ASD (mean age 3; 17 females, 47 males) while they were exposed to naturalistic-continuous speech. EEG power of frequency bands typically associated with phrase-level chunking (δ, 1-3 Hz), phonemic encoding (low-γ, 25-35 Hz), and top-down control (β, 12-20 Hz) were markedly reduced in ASD relative to typically developing (TD) children. Speech neural tracking by δ and θ (4-8 Hz) oscillations was also weaker in ASD compared with TD children. After controlling gaze-pattern differences, we found that the classical θ/γ coupling was replaced by an atypical β/γ coupling in children with ASD. This anomaly was the single most specific predictor of individual speech reception difficulties in ASD children. These findings suggest that early interventions (e.g., neurostimulation) targeting the disruption of β/γ coupling and the upregulation of θ/γ coupling could improve speech processing coordination in young children with ASD and help them engage in oral interactions.SIGNIFICANCE STATEMENT Very young children already present marked alterations of neural oscillatory activity in response to natural speech at the time of autism spectrum disorder (ASD) diagnosis. Hierarchical processing of phonemic-range and syllabic-range information (θ/γ coupling) is disrupted in ASD children. Abnormal bottom-up (low-γ) and top-down (low-β) coordination specifically predicts speech reception deficits in very young ASD children, and no other cognitive deficit.

Computational Methods for Single-Cell Proteomics

Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.

Visualizing scRNA-Seq Data at Population Scale with GloScope

Increasingly scRNA-Seq studies explore the heterogeneity of cell populations across different samples and its effect on an organism's phenotype. However, relatively few bioinformatic methods have been developed which adequately address the variation between samples for such population-level analyses. We propose a framework for representing the entire single-cell profile of a sample, which we call its GloScope representation. We implement GloScope on scRNA-Seq datasets from study designs ranging from 12 to over 300 samples. These examples demonstrate how GloScope allows researchers to perform essential bioinformatic tasks at the sample-level, in particular visualization and quality control assessment.

Activation priming and cytokine polyfunctionality modulate the enhanced functionality of low-affinity CD19 CAR T cells

We recently described a low-affinity second-generation CD19 chimeric antigen receptor (CAR) CAT that showed enhanced expansion, cytotoxicity, and antitumor efficacy compared with the high-affinity (FMC63-based) CAR used in tisagenlecleucel, in preclinical models. Furthermore, CAT demonstrated an excellent toxicity profile, enhanced in vivo expansion, and long-term persistence in a phase 1 clinical study. To understand the molecular mechanisms behind these properties of CAT CAR T cells, we performed a systematic in vitro characterization of the transcriptomic (RNA sequencing) and protein (cytometry by time of flight) changes occurring in T cells expressing low-affinity vs high-affinity CD19 CARs following stimulation with CD19-expressing cells. Our results show that CAT CAR T cells exhibit enhanced activation to CD19 stimulation and a distinct transcriptomic and protein profile, with increased activation and cytokine polyfunctionality compared with FMC63 CAR T cells. We demonstrate that the enhanced functionality of low-affinity CAT CAR T cells is a consequence of an antigen-dependent priming induced by residual CD19-expressing B cells present in the manufacture.

High-throughput interrogation of immune responses using the Human Immune Profiling Pipeline

The current abundance of immunotherapy clinical trials presents an opportunity to learn about the underlying mechanisms and pharmacodynamic effects of novel drugs on the human immune system. Here, we present a protocol to study how these immune responses impact clinical outcomes using large-scale high-throughput immune profiling of clinical cohorts. We describe the Human Immune Profiling Pipeline, which comprises an end-to-end solution from flow cytometry results to computational approaches and unsupervised patient clustering based on lymphocyte landscape. For complete details on the use and execution of this protocol, please refer to Lyudovyk et al. (2022).1.

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-1β 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.

Isoelectric point barcode and similarity analysis with the earth mover's distance for identification of species origin of raw meat

In this work, by combining the microcolumn isoelectric focusing (mIEF) and similarity analysis with the earth mover's distance (EMD) metric, we proposed the concept of isoelectric point (pI) barcode for the identification of species origin of raw meat. At first, we used the mIEF to analyze 14 meat species, including 8 species of livestock and 6 species of poultry, to generate 140 electropherograms of myoglobin/hemoglobin (Mb/Hb) markers. Secondly, we binarized the electropherograms and converted them into the pI barcodes that only showed the major Mb/Hb bands for the EMD analysis. Thirdly, we efficiently developed the barcode database of 14 meat species and successfully used the EMD method to identify 9 meat products thanks to the high throughput of mIEF and the simplified format of the barcode for similarity analysis. The developed method had the merits of facility, rapidity and low cost. The developed concept and method had evident potential to the facile identification of meat species.

Oscillatory ERK Signaling and Morphology Determine Heterogeneity of Breast Cancer Cell Chemotaxis via MEK-ERK and p38-MAPK Signaling Pathways

Chemotaxis, regulated by oscillatory signals, drives critical processes in cancer metastasis. Crucial chemoattractant molecules in breast cancer, CXCL12 and EGF, drive the activation of ERK and Akt. Regulated by feedback and crosstalk mechanisms, oscillatory signals in ERK and Akt control resultant changes in cell morphology and chemotaxis. While commonly studied at the population scale, metastasis arises from small numbers of cells that successfully disseminate, underscoring the need to analyze processes that cancer cells use to connect oscillatory signaling to chemotaxis at single-cell resolution. Furthermore, little is known about how to successfully target fast-migrating cells to block metastasis. We investigated to what extent oscillatory networks in single cells associate with heterogeneous chemotactic responses and how targeted inhibitors block signaling processes in chemotaxis. We integrated live, single-cell imaging with time-dependent data processing to discover oscillatory signal processes defining heterogeneous chemotactic responses. We identified that short ERK and Akt waves, regulated by MEK-ERK and p38-MAPK signaling pathways, determine the heterogeneous random migration of cancer cells. By comparison, long ERK waves and the morphological changes regulated by MEK-ERK signaling, determine heterogeneous directed motion. This study indicates that treatments against chemotaxis in consider must interrupt oscillatory signaling.

A statistical framework for high-content phenotypic profiling using cellular feature distributions

High-content screening (HCS) uses microscopy images to generate phenotypic profiles of cell morphological data in high-dimensional feature space. While HCS provides detailed cytological information at single-cell resolution, these complex datasets are usually aggregated into summary statistics that do not leverage patterns of biological variability within cell populations. Here we present a broad-spectrum HCS analysis system that measures image-based cell features from 10 cellular compartments across multiple assay panels. We introduce quality control measures and statistical strategies to streamline and harmonize the data analysis workflow, including positional and plate effect detection, biological replicates analysis and feature reduction. We also demonstrate that the Wasserstein distance metric is superior over other measures to detect differences between cell feature distributions. With this workflow, we define per-dose phenotypic fingerprints for 65 mechanistically diverse compounds, provide phenotypic path visualizations for each compound and classify compounds into different activity groups.

A pipeline for malignancy and therapy agnostic assessment of cancer drug response using cell mass measurements

Functional precision medicine offers a promising complement to genomics-based cancer therapy guidance by testing drug efficacy directly on a patient's tumor cells. Here, we describe a workflow that utilizes single-cell mass measurements with inline brightfield imaging and machine-learning based image classification to broaden the clinical utility of such functional testing for cancer. Using these image-curated mass measurements, we characterize mass response signals for 60 different drugs with various mechanisms of action across twelve different cell types, demonstrating an improved ability to detect response for several slow acting drugs as compared with standard cell viability assays. Furthermore, we use this workflow to assess drug responses for various primary tumor specimen formats including blood, bone marrow, fine needle aspirates (FNA), and malignant fluids, all with reports generated within two days and with results consistent with patient clinical responses. The combination of high-resolution measurement, broad drug and malignancy applicability, and rapid return of results offered by this workflow suggests that it is well-suited to performing clinically relevant functional assessment of cancer drug response.

In Situ-Forming Collagen-Hyaluronate Semi-Interpenetrating Network Hydrogel Enhances Corneal Defect Repair

Purpose

Millions worldwide suffer vision impairment or blindness from corneal injury, and there remains an urgent need for a more effective and accessible way to treat corneal defects. We have designed and characterized an in situ-forming semi-interpenetrating polymer network (SIPN) hydrogel using biomaterials widely used in ophthalmology and medicine.

Methods

The SIPN was formed by cross-linking collagen type I with bifunctional polyethylene glycol using N-hydroxysuccinimide ester chemistry in the presence of linear hyaluronic acid (HA). Gelation time and the mechanical, optical, swelling, and degradation properties of the SIPN were assessed. Cytocompatibility with human corneal epithelial cells and corneal stromal stem cells (CSSCs) was determined in vitro, as was the spatial distribution of encapsulated CSSCs within the SIPN. In vivo wound healing was evaluated by multimodal imaging in an anterior lamellar keratectomy injury model in rabbits, followed by immunohistochemical analysis of treated and untreated tissues.

Results

The collagen-hyaluronate SIPN formed in situ without an external energy source and demonstrated mechanical and optical properties similar to the cornea. It was biocompatible with human corneal cells, enhancing CSSC viability when compared with collagen gel controls and preventing encapsulated CSSC sedimentation. In vivo application of the SIPN significantly reduced stromal defect size compared with controls after 7 days and promoted multilayered epithelial regeneration.

Conclusions

This in situ-forming SIPN hydrogel may be a promising alternative to keratoplasty and represents a step toward expanding treatment options for patients suffering from corneal injury.

Translational Relevance

We detail the synthesis and initial characterization of an SIPN hydrogel as a potential alternative to lamellar keratoplasty and a tunable platform for further development in corneal tissue engineering and therapeutic cell delivery.

A Simple and Robust Single-Step Method for CAR-Vδ1 γδT Cell Expansion and Transduction for Cancer Immunotherapy

The γδT cell subset of peripheral lymphocytes exhibits potent cancer antigen recognition independent of classical peptide MHC complexes, making it an attractive candidate for allogeneic cancer adoptive immunotherapy. The Vδ1-T cell receptor (TCR)-expressing subset of peripheral γδT cells has remained enigmatic compared to its more prevalent Vγ9Vδ2-TCR and αβ-TCR-expressing counterparts. It took until 2021 before a first patient was dosed with an allogeneic adoptive Vδ1 cell product despite pre-clinical promise for oncology indications stretching back to the 1980s. A contributing factor to the paucity of clinical progress with Vδ1 cells is the lack of robust, consistent and GMP-compatible expansion protocols. Herein we describe a reproducible one-step, clinically translatable protocol for Vδ1-γδT cell expansion from peripheral blood mononuclear cells (PBMCs), that is further compatible with high-efficiency gene engineering for immunotherapy purposes. Briefly, αβTCR- and CD56-depleted PBMC stimulation with known-in-the-art T cell stimulators, anti-CD3 mAb (clone: OKT-3) and IL-15, leads to robust Vδ1 cell expansion of high purity and innate-like anti-tumor efficacy. These Vδ1 cells can be virally transduced to express chimeric antigen receptors (CARs) using standard techniques, and the CAR-Vδ1 exhibit antigen-specific persistence, cytotoxicity and produce IFN-γ. Practicable, GMP-compatible engineered Vδ1 cell expansion methods will be crucial to the wide-spread clinical testing of these cells for oncology indications.

cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies

Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses. However, in many cases the full potential of co-analyses is not reached due to technical variance between data from different experimental batches. Here, we present cyCombine, a method to robustly integrate cytometry data from different batches, experiments, or even different experimental techniques, such as CITE-seq, flow cytometry, and mass cytometry. We demonstrate that cyCombine maintains the biological variance and the structure of the data, while minimizing the technical variance between datasets. cyCombine does not require technical replicates across datasets, and computation time scales linearly with the number of cells, allowing for integration of massive datasets. Robust, accurate, and scalable integration of cytometry data enables integration of multiple datasets for primary data analyses and the validation of results using public datasets.

High-dimensional functional phenotyping of preclinical human CAR T cells using mass cytometry

Here, we present a comprehensive protocol for the generation and functional characterization of chimeric antigen receptor (CAR) T cells and their products by mass cytometry in a reproducible and scalable manner. We describe the production of CAR T cells from human peripheral blood mononuclear cells. We then detail a three-step staining protocol with metal-labeled antibodies and the subsequent mass cytometry analysis. This protocol allows simultaneous characterization of CAR T cell intracellular signaling, activation, proliferation, cytokine production, and phenotype in a single assay.

Prdm6 controls heart development by regulating neural crest cell differentiation and migration

The molecular mechanisms that drive the acquisition of distinct neural crest cell (NCC) fates is still poorly understood. Here, we identified Prdm6 as an epigenetic modifier that temporally and spatially regulates the expression of NCC specifiers and determines the fate of a subset of migrating cardiac NCCs (CNCCs). Using transcriptomic analysis and genetic and fate mapping approaches in transgenic mice, we showed that disruption of Prdm6 was associated with impaired CNCC differentiation, delamination, and migration and led to patent ductus arteriosus (DA) and ventricular noncompaction. Bulk and single-cell RNA-Seq analyses of the DA and CNCCs identified Prdm6 as a regulator of a network of CNCC specification genes, including Wnt1, Tfap2b, and Sox9. Loss of Prdm6 in CNCCs diminished its expression in the pre-epithelial–mesenchymal transition (pre-EMT) cluster, resulting in the retention of NCCs in the dorsal neural tube. This defect was associated with diminished H4K20 monomethylation and G1-S progression and augmented Wnt1 transcript levels in pre-EMT and neural tube clusters, which we showed was the major driver of the impaired CNCC migration. Altogether, these findings revealed Prdm6 as a key regulator of CNCC differentiation and migration and identified Prdm6 and its regulated network as potential targets for the treatment of congenital heart diseases.

Multivariate FMRI Signatures of Learning in a Hebb Repetition Paradigm With Tone Sequences

Important information from the environment often arrives to the brain in temporally extended sequences. Language, music, actions, and complex events generally unfold over time. When such informational sequences exceed the limited capacity of working memory, the human brain relies on its ability to accumulate information in long-term memory over several encounters with a complex stimulus. A longstanding question in psychology and neuroscience is whether the neural structures associated with working memory storage—often viewed as capacity limited and temporary—have any builtin ability to store information across longer temporal delays. According to the classic Hebbian dual memory theory, temporally local “activity traces” underlie immediate perception and working memory, whereas “structural traces” undergird long-term learning. Here we examine whether brain structures known to be involved in working maintenance of auditory sequences, such as area Spt, also show evidence of memory persistence across trials. We used representational similarity analysis (RSA) and the Hebb repetition paradigm with supracapacity tonal sequences to test whether repeated sequences have distinguishable multivoxel activity patterns in the auditory-motor networks of the brain. We found that, indeed, area Spt and other nodes of the auditory dorsal stream show multivoxel patterns for tone sequences that become gradually more distinct with repetition during working memory for supracapacity tone-sequences. The findings suggest that the structures are important for working memory are not “blank slates,” wiped clean from moment to moment, but rather encode information in a way can lead to cross-trial persistence.

New interpretable machine-learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy

We introduce a new method for single-cell cytometry studies, FAUST, which performs unbiased cell population discovery and annotation. FAUST processes experimental data on a per-sample basis and returns biologically interpretable cell phenotypes, making it well suited for the analysis of complex datasets. We provide simulation studies that compare FAUST with existing methodology, exemplifying its strength. We apply FAUST to data from a Merkel cell carcinoma anti-PD-1 trial and discover pre-treatment effector memory T cell correlates of outcome co-expressing PD-1, HLA-DR, and CD28. Using FAUST, we then validate these correlates in cryopreserved peripheral blood mononuclear cell samples from the same study, as well as an independent CyTOF dataset from a published metastatic melanoma trial. Finally, we show how FAUST's phenotypes can be used to perform cross-study data integration in the presence of diverse staining panels. Together, these results establish FAUST as a powerful new approach for unbiased discovery in single-cell cytometry.

•

An interpretable machine-learning method for cytometry data analysis is developed

•

Using this, candidate biomarkers of response to therapy are identified and visualized

•

The method is used to validate our findings on two additional cytometry datasets

•

It is shown how to integrate findings across datasets with heterogeneous marker panels

Our article introduces a new method, FAUST, which combines novel algorithms for clustering, cluster matching, variable selection, and feature selection. While these algorithms were developed for application to high-dimensional single-cell data—and our article validates this application area with multiple case studies—they are general purpose and can be applied to any collection of related real-valued matrices one wishes to partition.

Some useful features of these algorithms to the broader data science community include the following: they estimate the number of clusters across a dataset, they can be applied independently to each matrix in the set of matrices one wishes to cluster, they match clusters across matrices on the basis of data-driven annotations, and the annotations are interpretable in relation to the initial measurement variables. We provide an open-source implementation of our method, https://github.com/RGLab/FAUST, targeting data structures optimized for use in cytometry data analysis.

Cellular and humoral immune responses following SARS-CoV-2 mRNA vaccination in patients with multiple sclerosis on anti-CD20 therapy

SARS-CoV-2 messenger RNA vaccination in healthy individuals generates immune protection against COVID-19. However, little is known about SARS-CoV-2 mRNA vaccine-induced responses in immunosuppressed patients. We investigated induction of antigen-specific antibody, B cell and T cell responses longitudinally in patients with multiple sclerosis (MS) on anti-CD20 antibody monotherapy (n = 20) compared with healthy controls (n = 10) after BNT162b2 or mRNA-1273 mRNA vaccination. Treatment with anti-CD20 monoclonal antibody (aCD20) significantly reduced spike-specific and receptor-binding domain (RBD)-specific antibody and memory B cell responses in most patients, an effect ameliorated with longer duration from last aCD20 treatment and extent of B cell reconstitution. By contrast, all patients with MS treated with aCD20 generated antigen-specific CD4 and CD8 T cell responses after vaccination. Treatment with aCD20 skewed responses, compromising circulating follicular helper T (TFH) cell responses and augmenting CD8 T cell induction, while preserving type 1 helper T (TH1) cell priming. Patients with MS treated with aCD20 lacking anti-RBD IgG had the most severe defect in circulating TFH responses and more robust CD8 T cell responses. These data define the nature of the SARS-CoV-2 vaccine-induced immune landscape in aCD20-treated patients and provide insights into coordinated mRNA vaccine-induced immune responses in humans. Our findings have implications for clinical decision-making and public health policy for immunosuppressed patients including those treated with aCD20.

Multiplexed single-cell analysis of organoid signaling networks

Organoids are biomimetic tissue models comprising multiple cell types and cell states. Post-translational modification (PTM) signaling networks control cellular phenotypes and are frequently dysregulated in diseases such as cancer. Although signaling networks vary across cell types, there are limited techniques to study cell type-specific PTMs in heterocellular organoids. Here, we present a multiplexed mass cytometry (MC) protocol for single-cell analysis of PTM signaling and cell states in organoids and organoids co-cultured with fibroblasts and leukocytes. We describe how thiol-reactive organoid barcoding in situ (TOBis) enables 35-plex and 126-plex single-cell comparison of organoid cultures and provide a cytometry by time of flight (CyTOF) signaling analysis pipeline (CyGNAL) for computing cell type-specific PTM signaling networks. The TOBis MC protocol takes ~3 d from organoid fixation to data acquisition and can generate single-cell data for >40 antibodies from millions of cells across 126 organoid cultures in a single MC run.

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease-specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders EXpanding (T-REX) was created to identify changes in both rare and common cells across human immune monitoring settings. T-REX identified cells with highly similar phenotypes that localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized MHCII tetramer reagents that mark rhinovirus-specific CD4+ cells were left out during analysis and then used to test whether T-REX identified biologically significant cells. T-REX identified rhinovirus-specific CD4+ T cells based on phenotypically homogeneous cells expanding by ≥95% following infection. T-REX successfully identified hotspots of virus-specific T cells by comparing infection (day 7) to either pre-infection (day 0) or post-infection (day 28) samples. Plotting the direction and degree of change for each individual donor provided a useful summary view and revealed patterns of immune system behavior across immune monitoring settings. For example, the magnitude and direction of change in some COVID-19 patients was comparable to blast crisis acute myeloid leukemia patients undergoing a complete response to chemotherapy. Other COVID-19 patients instead displayed an immune trajectory like that seen in rhinovirus infection or checkpoint inhibitor therapy for melanoma. The T-REX algorithm thus rapidly identifies and characterizes mechanistically significant cells and places emerging diseases into a systems immunology context for comparison to well-studied immune changes.

CD8+ T cells contribute to survival in patients with COVID-19 and hematologic cancer

Patients with cancer have high mortality from coronavirus disease 2019 (COVID-19), and the immune parameters that dictate clinical outcomes remain unknown. In a cohort of 100 patients with cancer who were hospitalized for COVID-19, patients with hematologic cancer had higher mortality relative to patients with solid cancer. In two additional cohorts, flow cytometric and serologic analyses demonstrated that patients with solid cancer and patients without cancer had a similar immune phenotype during acute COVID-19, whereas patients with hematologic cancer had impairment of B cells and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific antibody responses. Despite the impaired humoral immunity and high mortality in patients with hematologic cancer who also have COVID-19, those with a greater number of CD8 T cells had improved survival, including those treated with anti-CD20 therapy. Furthermore, 77% of patients with hematologic cancer had detectable SARS-CoV-2-specific T cell responses. Thus, CD8 T cells might influence recovery from COVID-19 when humoral immunity is deficient. These observations suggest that CD8 T cell responses to vaccination might provide protection in patients with hematologic cancer even in the setting of limited humoral responses.

Quantum chaos and physical distance between quantum states

We show that there is genuine chaos in quantum dynamics by introducing a physical distance between two quantum states. Qualitatively different from existing distances for quantum states, for example, the Fubini-Study distance, the physical distance between two mutually orthogonal quantum states, can be very small. As a result, two quantum states, which are initially very close by physical distance, can diverge from each other during the ensuing quantum dynamical evolution. We are able to use physical distance to define the quantum Lyapunov exponent and the quantum chaos measure. The latter leads to a quantum analog of the classical Poincaré section, which maps out the regions where quantum dynamics is regular and the regions where it is chaotic. Three different systems-a kicked rotor, the three-site Bose-Hubbard model, and the spin-1/2 XXZ model-are used to illustrate our results.

Concepts and Applications of Information Theory to Immuno-Oncology

Recent successes of immune-modulating therapies for cancer have stimulated research on information flow within the immune system and, in turn, clinical applications of concepts from information theory. Through information theory, one can describe and formalize, in a mathematically rigorous fashion, the function of interconnected components of the immune system in health and disease. Specifically, using concepts including entropy, mutual information, and channel capacity, one can quantify the storage, transmission, encoding, and flow of information within and between cellular components of the immune system on multiple temporal and spatial scales. To understand, at the quantitative level, immune signaling function and dysfunction in cancer, we present a methodology-oriented review of information-theoretic treatment of biochemical signal transduction and transmission coupled with mathematical modeling.

Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response

The heterogeneity of breast cancer plays a major role in drug response and resistance and has been extensively characterized at the genomic level. Here, a single-cell breast cancer mass cytometry (BCMC) panel is optimized to identify cell phenotypes and their oncogenic signalling states in a biobank of patient-derived tumour xenograft (PDTX) models representing the diversity of human breast cancer. The BCMC panel identifies 13 cellular phenotypes (11 human and 2 murine), associated with both breast cancer subtypes and specific genomic features. Pre-treatment cellular phenotypic composition is a determinant of response to anticancer therapies. Single-cell profiling also reveals drug-induced cellular phenotypic dynamics, unravelling previously unnoticed intra-tumour response diversity. The comprehensive view of the landscapes of cellular phenotypic heterogeneity in PDTXs uncovered by the BCMC panel, which is mirrored in primary human tumours, has profound implications for understanding and predicting therapy response and resistance.

Single-cell longitudinal analysis of SARS-CoV-2 infection in human airway epithelium identifies target cells, alterations in gene expression, and cell state changes

There are currently limited Food and Drug Administration (FDA)-approved drugs and vaccines for the treatment or prevention of Coronavirus Disease 2019 (COVID-19). Enhanced understanding of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and pathogenesis is critical for the development of therapeutics. To provide insight into viral replication, cell tropism, and host-viral interactions of SARS-CoV-2, we performed single-cell (sc) RNA sequencing (RNA-seq) of experimentally infected human bronchial epithelial cells (HBECs) in air-liquid interface (ALI) cultures over a time course. This revealed novel polyadenylated viral transcripts and highlighted ciliated cells as a major target at the onset of infection, which we confirmed by electron and immunofluorescence microscopy. Over the course of infection, the cell tropism of SARS-CoV-2 expands to other epithelial cell types including basal and club cells. Infection induces cell-intrinsic expression of type I and type III interferons (IFNs) and interleukin (IL)-6 but not IL-1. This results in expression of interferon-stimulated genes (ISGs) in both infected and bystander cells. This provides a detailed characterization of genes, cell types, and cell state changes associated with SARS-CoV-2 infection in the human airway.

Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data

Cancer immunotherapy, specifically immune checkpoint blockade, has been found to be effective in the treatment of metastatic cancers. However, only a subset of patients achieve clinical responses. Elucidating pretreatment biomarkers predictive of sustained clinical response is a major research priority. Another research priority is evaluating changes in the immune system before and after treatment in responders vs. nonresponders. Our group has been studying immune networks as an accurate reflection of the global immune state. Flow cytometry (FACS, fluorescence-activated cell sorting) data characterizing immune cell panels in peripheral blood mononuclear cells (PBMC) from gastroesophageal adenocarcinoma (GEA) patients were used to analyze changes in immune networks in this setting. Here, we describe a novel computational pipeline to perform secondary analyses of FACS data using systems biology/machine learning techniques and concepts. The pipeline is centered around comparative Bayesian network analyses of immune networks and is capable of detecting strong signals that conventional methods (such as FlowJo manual gating) might miss. Future studies are planned to validate and follow up the immune biomarkers (and combinations/interactions thereof) associated with clinical responses identified with this computational pipeline.

CD8 T cells compensate for impaired humoral immunity in COVID-19 patients with hematologic cancer

Cancer patients have increased morbidity and mortality from Coronavirus Disease 2019 (COVID-19), but the underlying immune mechanisms are unknown. In a cohort of 100 cancer patients hospitalized for COVID-19 at the University of Pennsylvania Health System, we found that patients with hematologic cancers had a significantly higher mortality relative to patients with solid cancers after accounting for confounders including ECOG performance status and active cancer status. We performed flow cytometric and serologic analyses of 106 cancer patients and 113 non-cancer controls from two additional cohorts at Penn and Memorial Sloan Kettering Cancer Center. Patients with solid cancers exhibited an immune phenotype similar to non-cancer patients during acute COVID-19 whereas patients with hematologic cancers had significant impairment of B cells and SARS-CoV-2-specific antibody responses. High dimensional analysis of flow cytometric data revealed 5 distinct immune phenotypes. An immune phenotype characterized by CD8 T cell depletion was associated with a high viral load and the highest mortality of 71%, among all cancer patients. In contrast, despite impaired B cell responses, patients with hematologic cancers and preserved CD8 T cells had a lower viral load and mortality. These data highlight the importance of CD8 T cells in acute COVID-19, particularly in the setting of impaired humoral immunity. Further, depletion of B cells with anti-CD20 therapy resulted in almost complete abrogation of SARS-CoV-2-specific IgG and IgM antibodies, but was not associated with increased mortality compared to other hematologic cancers, when adequate CD8 T cells were present. Finally, higher CD8 T cell counts were associated with improved overall survival in patients with hematologic cancers. Thus, CD8 T cells likely compensate for deficient humoral immunity and influence clinical recovery of COVID-19. These observations have important implications for cancer and COVID-19-directed treatments, immunosuppressive therapies, and for understanding the role of B and T cells in acute COVID-19.

Biomarkers for the Diagnosis of Allergic Bronchopulmonary Aspergillosis in Cystic Fibrosis: A Systematic Review and Meta-Analysis

BACKGROUND

Allergic bronchopulmonary aspergillosis (ABPA) is a hypersensitivity reaction to Aspergillus fumigatus and impacts 10% of individuals with cystic fibrosis (CF). A diagnosis of ABPA is challenging to establish in CF owing to overlapping clinical and radiologic features with CF lung disease. Recent studies have identified blood tests, imaging, and other biomarkers that may be useful for diagnosis.

OBJECTIVE

To summarize biomarkers that can aid in the diagnosis of ABPA in CF patients and to quantify their diagnostic accuracy through meta-analysis.

METHODS

We searched MEDLINE, EMBASE, and the Cochrane Controlled Register of Trials and included studies that used a laboratory technique or imaging modality in CF patients diagnosed with ABPA. Pooled sensitivity and specificity were calculated using a hierarchical summary receiver operating characteristic model.

RESULTS

We identified 791 articles, of which 29 met our eligibility criteria and 9 were included in the meta-analysis. Hyperattenuating mucus on computed tomography (CT) scan (n = 3 studies; pooled sensitivity 62% and specificity 92%) and serum specific immunoglobulin E against recombinant Aspergillus funigatus antigens f4 (n = 6; 69%, 89%) and f6 (n = 6; 39%, 97%) demonstrated high specificity. Based on single studies, serum thymus and activation regulated chemokine (92%, 94%), stimulated basophil expression of CD203c (94%, 74%), the inverted mucoid impaction signal on magnetic resonance imaging (94%, 100%), and skin prick test with recombinant Aspergillus fumigatus f4 and/or f6 (100%, 100%) showed high sensitivity and specificity.

CONCLUSIONS

Recent studies have found promising biomarkers for diagnosing ABPA in CF. Further research is needed to improve our understanding of their utility in diagnosis and disease monitoring.

Optimal Estimation of Wasserstein Distance on A Tree with An Application to Microbiome Studies

The weighted UniFrac distance, a plug-in estimator of the Wasserstein distance of read counts on a tree, has been widely used to measure the microbial community difference in microbiome studies. Our investigation however shows that such a plug-in estimator, although intuitive and commonly used in practice, suffers from potential bias. Motivated by this finding, we study the problem of optimal estimation of the Wasserstein distance between two distributions on a tree from the sampled data in the high-dimensional setting. The minimax rate of convergence is established. To overcome the bias problem, we introduce a new estimator, referred to as the moment-screening estimator on a tree (MET), by using implicit best polynomial approximation that incorporates the tree structure. The new estimator is computationally efficient and is shown to be minimax rate-optimal. Numerical studies using both simulated and real biological datasets demonstrate the practical merits of MET, including reduced biases and statistically more significant differences in microbiome between the inactive Crohn's disease patients and the normal controls.

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders Expanding (T-REX) was created to identify changes in both very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized reagents used to detect the rhinovirus-specific CD4+ cells, MHCII tetramers, were not used during unsupervised analysis and instead 'left out' to serve as a test of whether T-REX identified biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4+ T cells based on these cells being a distinct phenotype that expanded by ≥95% following infection. T-REX successfully identified hotspots containing virus-specific T cells using pairs of samples comparing Day 7 of infection to samples taken either prior to infection (Day 0) or after clearing the infection (Day 28). Mapping pairwise comparisons in samples according to both the direction and degree of change provided a framework to compare systems level immune changes during infectious disease or therapy response. This revealed that the magnitude and direction of systemic immune change in some COVID-19 patients was comparable to that of blast crisis acute myeloid leukemia patients undergoing induction chemotherapy and characterized the identity of the immune cells that changed the most. Other COVID-19 patients instead matched an immune trajectory like that of individuals with rhinovirus infection or melanoma patients receiving checkpoint inhibitor therapy. T-REX analysis of paired blood samples provides an approach to rapidly identify and characterize mechanistically significant cells and to place emerging diseases into a systems immunology context.

optimalFlow: optimal transport approach to flow cytometry gating and population matching

Background

Data obtained from flow cytometry present pronounced variability due to biological and technical reasons. Biological variability is a well-known phenomenon produced by measurements on different individuals, with different characteristics such as illness, age, sex, etc. The use of different settings for measurement, the variation of the conditions during experiments and the different types of flow cytometers are some of the technical causes of variability. This mixture of sources of variability makes the use of supervised machine learning for identification of cell populations difficult. The present work is conceived as a combination of strategies to facilitate the task of supervised gating.

Results

We propose optimalFlowTemplates, based on a similarity distance and Wasserstein barycenters, which clusters cytometries and produces prototype cytometries for the different groups. We show that supervised learning, restricted to the new groups, performs better than the same techniques applied to the whole collection. We also present optimalFlowClassification, which uses a database of gated cytometries and optimalFlowTemplates to assign cell types to a new cytometry. We show that this procedure can outperform state of the art techniques in the proposed datasets. Our code is freely available as optimalFlow, a Bioconductor R package at https://bioconductor.org/packages/optimalFlow.

Conclusions

optimalFlowTemplates + optimalFlowClassification addresses the problem of using supervised learning while accounting for biological and technical variability. Our methodology provides a robust automated gating workflow that handles the intrinsic variability of flow cytometry data well. Our main innovation is the methodology itself and the optimal transport techniques that we apply to flow cytometry analysis.

Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications

Coronavirus disease 2019 (COVID-19) is currently a global pandemic, but human immune responses to the virus remain poorly understood. We used high-dimensional cytometry to analyze 125 COVID-19 patients and compare them with recovered and healthy individuals. Integrated analysis of ~200 immune and ~50 clinical features revealed activation of T cell and B cell subsets in a proportion of patients. A subgroup of patients had T cell activation characteristic of acute viral infection and plasmablast responses reaching >30% of circulating B cells. However, another subgroup had lymphocyte activation comparable with that in uninfected individuals. Stable versus dynamic immunological signatures were identified and linked to trajectories of disease severity change. Our analyses identified three immunotypes associated with poor clinical trajectories versus improving health. These immunotypes may have implications for the design of therapeutics and vaccines for COVID-19.

Single-cell longitudinal analysis of SARS-CoV-2 infection in human airway epithelium

SARS-CoV-2, the causative agent of COVID-19, has tragically burdened individuals and institutions around the world. There are currently no approved drugs or vaccines for the treatment or prevention of COVID-19. Enhanced understanding of SARS-CoV-2 infection and pathogenesis is critical for the development of therapeutics. To reveal insight into viral replication, cell tropism, and host-viral interactions of SARS-CoV-2 we performed single-cell RNA sequencing of experimentally infected human bronchial epithelial cells (HBECs) in air-liquid interface cultures over a time-course. This revealed novel polyadenylated viral transcripts and highlighted ciliated cells as a major target of infection, which we confirmed by electron microscopy. Over the course of infection, cell tropism of SARS-CoV-2 expands to other epithelial cell types including basal and club cells. Infection induces cell-intrinsic expression of type I and type III IFNs and IL6 but not IL1. This results in expression of interferon-stimulated genes in both infected and bystander cells. We observe similar gene expression changes from a COVID-19 patient ex vivo. In addition, we developed a new computational method termed CONditional DENSity Embedding (CONDENSE) to characterize and compare temporal gene dynamics in response to infection, which revealed genes relating to endothelin, angio-genesis, interferon, and inflammation-causing signaling pathways. In this study, we conducted an in-depth analysis of SARS-CoV-2 infection in HBECs and a COVID-19 patient and revealed genes, cell types, and cell state changes associated with infection.

Enhanced mitochondrial fission suppresses signaling and metastasis in triple-negative breast cancer

BACKGROUND

Mitochondrial dynamics underlies malignant transformation, cancer progression, and response to treatment. Current research presents conflicting evidence for functions of mitochondrial fission and fusion in tumor progression. Here, we investigated how mitochondrial fission and fusion states regulate underlying processes of cancer progression and metastasis in triple-negative breast cancer (TNBC).

METHODS

We enforced mitochondrial fission and fusion states through chemical or genetic approaches and measured migration and invasion of TNBC cells in 2D and 3D in vitro models. We also utilized kinase translocation reporters (KTRs) to identify single cell effects of mitochondrial state on signaling cascades, PI3K/Akt/mTOR and Ras/Raf/MEK/ERK, commonly activated in TNBC. Furthermore, we determined effects of fission and fusion states on metastasis, bone destruction, and signaling in mouse models of breast cancer.

RESULTS

Enforcing mitochondrial fission through chemical or genetic approaches inhibited migration, invasion, and metastasis in TNBC. Breast cancer cells with predominantly fissioned mitochondria exhibited reduced activation of Akt and ERK both in vitro and in mouse models of breast cancer. Treatment with leflunomide, a potent activator of mitochondrial fusion proteins, overcame inhibitory effects of fission on migration, signaling, and metastasis. Mining existing datasets for breast cancer revealed that increased expression of genes associated with mitochondrial fission correlated with improved survival in human breast cancer.

CONCLUSIONS

In TNBC, mitochondrial fission inhibits cellular processes and signaling pathways associated with cancer progression and metastasis. These data suggest that therapies driving mitochondrial fission may benefit patients with breast cancer.

Cell-type-specific signaling networks in heterocellular organoids

Despite the widespread adoption of organoids as biomimetic tissue models, methods to comprehensively analyze cell-type-specific post-translational modification (PTM) signaling networks in organoids are absent. Here, we report multivariate single-cell analysis of such networks in organoids and organoid cocultures. Simultaneous analysis by mass cytometry of 28 PTMs in >1 million single cells derived from small intestinal organoids reveals cell-type- and cell-state-specific signaling networks in stem, Paneth, enteroendocrine, tuft and goblet cells, as well as enterocytes. Integrating single-cell PTM analysis with thiol-reactive organoid barcoding in situ (TOBis) enables high-throughput comparison of signaling networks between organoid cultures. Cell-type-specific PTM analysis of colorectal cancer organoid cocultures reveals that shApc, KrasG12D and Trp53R172H cell-autonomously mimic signaling states normally induced by stromal fibroblasts and macrophages. These results demonstrate how standard mass cytometry workflows can be modified to perform high-throughput multivariate cell-type-specific signaling analysis of healthy and cancerous organoids.

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.

Regulation of mitochondrial fragmentation in microvascular endothelial cells isolated from the SU5416/hypoxia model of pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a morbid disease characterized by progressive right ventricle (RV) failure due to elevated pulmonary artery pressures (PAP). In PAH, histologically complex vaso-occlusive lesions in the pulmonary vasculature contribute to elevated PAP. However, the mechanisms underlying dysfunction of the microvascular endothelial cells (MVECs) that comprise a significant portion of these lesions are not well understood. We recently showed that MVECs isolated from the Sugen/hypoxia (SuHx) rat experimental model of PAH (SuHx-MVECs) exhibit increases in migration/proliferation, mitochondrial reactive oxygen species (ROS; mtROS) production, intracellular calcium levels ([Ca2+]i), and mitochondrial fragmentation. Furthermore, quenching mtROS with the targeted antioxidant MitoQ attenuated basal [Ca2+]i, migration and proliferation; however, whether increased mtROS-induced [Ca2+]i entry affected mitochondrial morphology was not clear. In this study, we sought to better understand the relationship between increased ROS, [Ca2+]i, and mitochondrial morphology in SuHx-MVECs. We measured changes in mitochondrial morphology at baseline and following inhibition of mtROS, with the targeted antioxidant MitoQ, or transient receptor potential vanilloid-4 (TRPV4) channels, which we previously showed were responsible for mtROS-induced increases in [Ca2+]i in SuHx-MVECs. Quenching mtROS or inhibiting TRPV4 attenuated fragmentation in SuHx-MVECs. Conversely, inducing mtROS production in MVECs from normoxic rats (N-MVECs) increased fragmentation. Ca2+ entry induced by the TRPV4 agonist GSK1017920A was significantly increased in SuHx-MVECs and was attenuated with MitoQ treatment, indicating that mtROS contributes to increased TRPV4 activity in SuHx-MVECs. Basal and maximal respiration were depressed in SuHx-MVECs, and inhibiting mtROS, but not TRPV4, improved respiration in these cells. Collectively, our data show that, in SuHx-MVECs, mtROS production promotes TRPV4-mediated increases in [Ca2+]i, mitochondrial fission, and decreased mitochondrial respiration. These results suggest an important role for mtROS in driving MVEC dysfunction in PAH.

Engineering γδT cells limits tonic signaling associated with chimeric antigen receptors

Despite the benefits of chimeric antigen receptor (CAR)-T cell therapies against lymphoid malignancies, responses in solid tumors have been more limited and off-target toxicities have been more marked. Among the possible design limitations of CAR-T cells for cancer are unwanted tonic (antigen-independent) signaling and off-target activation. Efforts to overcome these hurdles have been blunted by a lack of mechanistic understanding. Here, we showed that single-cell analysis with time course mass cytometry provided a rapid means of assessing CAR-T cell activation. We compared signal transduction in expanded T cells to that in T cells transduced to express second-generation CARs and found that cell expansion enhanced the response to stimulation. However, expansion also induced tonic signaling and reduced network plasticity, which were associated with expression of the T cell exhaustion markers PD-1 and TIM-3. Because this was most evident in pathways downstream of CD3ζ, we performed similar analyses on γδT cells that expressed chimeric costimulatory receptors (CCRs) lacking CD3ζ but containing DAP10 stimulatory domains. These CCR-γδT cells did not exhibit tonic signaling but were efficiently activated and mounted cytotoxic responses in the presence of CCR-specific stimuli or cognate leukemic cells. Single-cell signaling analysis enabled detailed characterization of CAR-T and CCR-T cell activation to better understand their functional activities. Furthermore, we demonstrated that CCR-γδT cells may offer the potential to avoid on-target, off-tumor toxicity and allo-reactivity in the context of myeloid malignancies.

Learning Single-Cell Distances from Cytometry Data

Recent years have seen an increased interest in employing data analysis techniques for the automated identification of cell populations in the field of cytometry. These techniques highly depend on the use of a distance metric, a function that quantifies the distances between single-cell measurements. In most cases, researchers simply use the Euclidean distance metric. In this article, we exploit the availability of single-cell labels to find an optimal Mahalanobis distance metric derived from the data. We show that such a Mahalanobis distance metric results in an improved identification of cell populations compared with the Euclidean distance metric. Once determined, it can be used for the analysis of multiple samples that were measured under the same experimental setup. We illustrate this approach for cytometry data from two different origins, that is, flow cytometry applied to microbial cells and mass cytometry for the analysis of human blood cells. We also illustrate that such a distance metric results in an improved identification of cell populations when clustering methods are employed. Generally, these results imply that the performance of data analysis techniques can be improved by using a more advanced distance metric. © 2019 International Society for Advancement of Cytometry.

Computational Immune Monitoring Reveals Abnormal Double-Negative T Cells Present across Human Tumor Types

Advances in single-cell biology have enabled measurements of >40 protein features on millions of immune cells within clinical samples. However, the data analysis steps following cell population identification are susceptible to bias, time-consuming, and challenging to compare across studies. Here, an ensemble of unsupervised tools was developed to evaluate four essential types of immune cell information, incorporate changes over time, and address diverse immune monitoring challenges. The four complementary properties characterized were (i) systemic plasticity, (ii) change in population abundance, (iii) change in signature population features, and (iv) novelty of cellular phenotype. Three systems immune monitoring studies were selected to challenge this ensemble approach. In serial biopsies of melanoma tumors undergoing targeted therapy, the ensemble approach revealed enrichment of double-negative (DN) T cells. Melanoma tumor-resident DN T cells were abnormal and phenotypically distinct from those found in nonmalignant lymphoid tissues, but similar to those found in glioblastoma and renal cell carcinoma. Overall, ensemble systems immune monitoring provided a robust, quantitative view of changes in both the system and cell subsets, allowed for transparent review by human experts, and revealed abnormal immune cells present across multiple human tumor types.

Quantitative assessment of cell population diversity in single-cell landscapes

Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such as expansion or shrinkage or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes and regional specification, identification and definition of altered cell infiltrates in tumorigenesis, and benchmarking batch-correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions and has broad utility for gaining insight into tissue-level perturbations at the single-cell resolution.

Reactive oxygen species induced Ca2+ influx via TRPV4 and microvascular endothelial dysfunction in the SU5416/hypoxia model of pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a lethal disease characterized by elevations in pulmonary arterial pressure, in part due to formation of occlusive lesions in the distal arterioles of the lung. These complex lesions may comprise multiple cell types, including endothelial cells (ECs). To better understand the molecular mechanisms underlying EC dysfunction in PAH, lung microvascular endothelial cells (MVECs) were isolated from normoxic rats (N-MVECs) and rats subjected to SU5416 plus hypoxia (SuHx), an experimental model of PAH. Compared with N-MVECs, MVECs isolated from SuHx rats (SuHx-MVECs) appeared larger and more spindle shaped morphologically and expressed canonical smooth muscle cell markers smooth muscle-specific α-actin and myosin heavy chain in addition to endothelial markers such as Griffonia simplicifolia and von Willebrand factor. SuHx-MVEC mitochondria were dysfunctional, as evidenced by increased fragmentation/fission, decreased oxidative phosphorylation, and increased reactive oxygen species (ROS) production. Functionally, SuHx-MVECs exhibited increased basal levels of intracellular calcium concentration ([Ca2+]i) and enhanced migratory and proliferative capacity. Treatment with global (TEMPOL) or mitochondria-specific (MitoQ) antioxidants decreased ROS levels and basal [Ca2]i in SuHx-MVECs. TEMPOL and MitoQ also decreased migration and proliferation in SuHx-MVECs. Additionally, inhibition of ROS-induced Ca2+ entry via pharmacologic blockade of transient receptor potential vanilloid-4 (TRPV4) attenuated [Ca2]i, migration, and proliferation. These findings suggest a role for mitochondrial ROS-induced Ca2+ influx via TRPV4 in promoting abnormal migration and proliferation in MVECs in this PAH model.