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

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.

Soft X-Ray Imaging of Cellular Carbon and Nitrogen Distributions in Heterocystous Cyanobacteria

Soft x-ray microscopy (SXM) is a minimally invasive technique for single-cell high-resolution imaging as well as the visualization of intracellular distributions of light elements such as carbon, nitrogen, and oxygen. We used SXM to observe photosynthesis and nitrogen fixation in the filamentous cyanobacterium Anabaena sp. PCC 7120, which can form heterocysts during nitrogen starvation. Statistical and spectroscopic analyses from SXM images around the K-absorption edge of nitrogen revealed a significant difference in the carbon-to-nitrogen (C/N) ratio between vegetative cells and heterocysts. Application of this analysis to soft x-ray images of Anabaena sp. PCC 7120 revealed inhomogenous C/N ratios in the cells. Furthermore, soft x-ray tomography of Anabaena sp. PCC 7120 revealed differing cellular C/N ratios, indicating different carbon and nitrogen distributions between vegetative cells and heterocysts in three dimensions.

Equivalence Testing of Complex Particle Size Distribution Profiles Based on Earth Mover's Distance

Particle size distribution (PSD) is an important property of particulates in drug products. In the evaluation of generic drug products formulated as suspensions, emulsions, and liposomes, the PSD comparisons between a test product and the branded product can provide useful information regarding in vitro and in vivo performance. Historically, the FDA has recommended the population bioequivalence (PBE) statistical approach to compare the PSD descriptors D50 and SPAN from test and reference products to support product equivalence. In this study, the earth mover's distance (EMD) is proposed as a new metric for comparing PSD particularly when the PSD profile exhibits complex distribution (e.g., multiple peaks) that is not accurately described by the D50 and SPAN descriptor. EMD is a statistical metric that measures the discrepancy (distance) between size distribution profiles without a prior assumption of the distribution. PBE is then adopted to perform statistical test to establish equivalence based on the calculated EMD distances. Simulations show that proposed EMD-based approach is effective in comparing test and reference profiles for equivalence testing and is superior compared to commonly used distance measures, e.g., Euclidean and Kolmogorov-Smirnov distances. The proposed approach was demonstrated by evaluating equivalence of cyclosporine ophthalmic emulsion PSDs that were manufactured under different conditions. Our results show that proposed approach can effectively pass an equivalent product (e.g., reference product against itself) and reject an inequivalent product (e.g., reference product against negative control), thus suggesting its usefulness in supporting bioequivalence determination of a test product to the reference product which both possess multimodal PSDs.

QFMatch: multidimensional flow and mass cytometry samples alignment

Part of the flow/mass cytometry data analysis process is aligning (matching) cell subsets between relevant samples. Current methods address this cluster-matching problem in ways that are either computationally expensive, affected by the curse of dimensionality, or fail when population patterns significantly vary between samples. Here, we introduce a quadratic form (QF)-based cluster matching algorithm (QFMatch) that is computationally efficient and accommodates cases where population locations differ significantly (or even disappear or appear) from sample to sample. We demonstrate the effectiveness of QFMatch by evaluating sample datasets from immunology studies. The algorithm is based on a novel multivariate extension of the quadratic form distance for the comparison of flow cytometry data sets. We show that this QF distance has attractive computational and statistical properties that make it well suited for analysis tasks that involve the comparison of flow/mass cytometry samples.

Characterizing cell subsets using marker enrichment modeling

Learning cell identity from high-content single-cell data presently relies on human experts. We present marker enrichment modeling (MEM), an algorithm that objectively describes cells by quantifying contextual feature enrichment and reporting a human- and machine-readable text label. MEM outperforms traditional metrics in describing immune and cancer cell subsets from fluorescence and mass cytometry. MEM provides a quantitative language to communicate characteristics of new and established cytotypes observed in complex tissues.