A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry

Proteomics applications in next generation induced pluripotent stem cell models

INTRODUCTION

Induced pluripotent stem (iPS) cell technology has transformed biomedical research. New opportunities now exist to create new organoids, microtissues, and body-on-a-chip systems for basic biology investigations and clinical translations.

AREAS COVERED

We discuss the utility of proteomics for attaining an unbiased view into protein expression changes during iPS cell differentiation, cell maturation, and tissue generation. The ability to discover cell-type specific protein markers during the differentiation and maturation of iPS-derived cells has led to new strategies to improve cell production yield and fidelity. In parallel, proteomic characterization of iPS-derived organoids is helping to realize the goal of bridging in vitro and in vivo systems.

EXPERT OPINIONS

We discuss some current challenges of proteomics in iPS cell research and future directions, including the integration of proteomic and transcriptomic data for systems-level analysis.

Circulation immune cell landscape in canonical pathogenesis of colorectal adenocarcinoma by CyTOF analysis

Current studies on the immune microenvironment of colorectal cancer (CRC) were mostly limited to the tissue level, lacking relevant studies in the peripheral blood, and failed to describe its alterations in the whole process of adenocarcinoma formation, especially of adenoma carcinogenesis. Here, we constructed a large-scale population cohort and used the CyTOF to explore the changes of various immune cell subsets in peripheral blood of CRC. We found monocytes and basophils cells were significantly higher in adenocarcinoma patients. Compared with early-stage CRC, effector CD4+T cells and naive B cells were higher in patients with lymph node metastasis, whereas the basophils were lower. We also performed random forest algorithm and found monocytes play the key role in carcinogenesis. Our study draws a peripheral blood immune cell landscape of the occurrence and development of CRC at the single-cell level and provides a reference for other researchers.

Partial cellular reprogramming: A deep dive into an emerging rejuvenation technology

Aging and age-associated disease are a major medical and societal burden in need of effective treatments. Cellular reprogramming is a biological process capable of modulating cell fate and cellular age. Harnessing the rejuvenating benefits without altering cell identity via partial cellular reprogramming has emerged as a novel translational strategy with therapeutic potential and strong commercial interests. Here, we explore the aging-related benefits of partial cellular reprogramming while examining limitations and future directions for the field.

Metabolic control of induced pluripotency

Pluripotent stem cells of the mammalian epiblast and their cultured counterparts-embryonic stem cells (ESCs) and epiblast stem cells (EpiSCs)-have the capacity to differentiate in all cell types of adult organisms. An artificial process of reactivation of the pluripotency program in terminally differentiated cells was established in 2006, which allowed for the generation of induced pluripotent stem cells (iPSCs). This iPSC technology has become an invaluable tool in investigating the molecular mechanisms of human diseases and therapeutic drug development, and it also holds tremendous promise for iPSC applications in regenerative medicine. Since the process of induced reprogramming of differentiated cells to a pluripotent state was discovered, many questions about the molecular mechanisms involved in this process have been clarified. Studies conducted over the past 2 decades have established that metabolic pathways and retrograde mitochondrial signals are involved in the regulation of various aspects of stem cell biology, including differentiation, pluripotency acquisition, and maintenance. During the reprogramming process, cells undergo major transformations, progressing through three distinct stages that are regulated by different signaling pathways, transcription factor networks, and inputs from metabolic pathways. Among the main metabolic features of this process, representing a switch from the dominance of oxidative phosphorylation to aerobic glycolysis and anabolic processes, are many critical stage-specific metabolic signals that control the path of differentiated cells toward a pluripotent state. In this review, we discuss the achievements in the current understanding of the molecular mechanisms of processes controlled by metabolic pathways, and vice versa, during the reprogramming process.

Synthetic genetic circuits to uncover the OCT4 trajectories of successful reprogramming of human fibroblasts

Reprogramming human fibroblasts to induced pluripotent stem cells (iPSCs) is inefficient, with heterogeneity among transcription factor (TF) trajectories driving divergent cell states. Nevertheless, the impact of TF dynamics on reprogramming efficiency remains uncharted. We develop a system that accurately reports OCT4 protein levels in live cells and use it to reveal the trajectories of OCT4 in successful reprogramming. Our system comprises a synthetic genetic circuit that leverages noise to generate a wide range of OCT4 trajectories and a microRNA targeting endogenous OCT4 to set total cellular OCT4 protein levels. By fusing OCT4 to a fluorescent protein, we are able to track OCT4 trajectories with clonal resolution via live-cell imaging. We discover that a supraphysiological, stable OCT4 level is required, but not sufficient, for efficient iPSC colony formation. Our synthetic genetic circuit design and high-throughput live-imaging pipeline are generalizable for investigating TF dynamics for other cell fate programming applications.

Mass cytometry as a tool in target validation and drug discovery

Mass cytometry provides highly multiparametric data at a single cell level, coupling the specificity and sensitivity of time-of-flight mass spectrometry with the single-cell throughput of flow cytometry. It offers great value in interrogating the potentially heterogenous impact that a drug may have on a biological system, allowing an investigator to capture not just changes in cell behavior, but how these changes may differ between cell subtypes. In this chapter, we review the technical details of the platform as well as its limitations, before describing our approach to planning and running a mass cytometry experiment. A series of method modules, spanning the staining process through to data cleaning, are described that are then combined to create three separate experiments. The first experiment illustrates a core process in mass cytometry: the validation and titration of a metal-conjugated antibody reporter. The second experiment explores the impact of a kinase inhibitor on cell cycle and apoptosis pathways of a human myeloma cell line. And the third experiment exploits the multiparametric capability of mass cytometry, by exploring the differential expression changes in a transcription factor upon drug treatment across the cellular compartments of a peripheral blood mononuclear cell sample.

Profiling A-to-I RNA editing during mouse somatic reprogramming at the single-cell level

Mouse somatic cells can be reprogrammed into induced pluripotent stem cells through a highly heterogeneous process regulated by numerous biological factors, including adenosine-to-inosine (A-to-I) RNA editing. In this study, we analyzed A-to-I RNA editing sites using a single-cell RNA sequencing (scRNA-seq) dataset with high-depth and full-length coverage. Our method revealed that A-to-I RNA editing frequency varied widely at the single-cell level and underwent dynamic changes. We also found that A-to-I RNA editing level was correlated with the expression of the RNA editing enzyme ADAR1. The analysis combined with gene ontology (GO) enrichment revealed that ADAR1-dependent A-to-I editing may downregulate the expression levels of Igtp, Irgm2, Mndal, Ifi202b, and Tapbp in the early stage, to inhibit the pathways of cellular response to interferon-beta and regulation of protein complex stability to promote mesenchymal-epithelial transition (MET). Notably, we identified a negative correlation between A-to-I RNA editing frequency and the expression of certain genes, such as Nras, Ube2l6, Zfp987, and Adsl.

Deep Immunophenotyping of Human Whole Blood by Standardized Multi-parametric Flow Cytometry Analyses

Immunophenotyping is proving crucial to understanding the role of the immune system in health and disease. High-throughput flow cytometry has been used extensively to reveal changes in immune cell composition and function at the single-cell level. Here, we describe six optimized 11-color flow cytometry panels for deep immunophenotyping of human whole blood. A total of 51 surface antibodies, which are readily available and validated, were selected to identify the key immune cell populations and evaluate their functional state in a single assay. The gating strategies for effective flow cytometry data analysis are included in the protocol. To ensure data reproducibility, we provide detailed procedures in three parts, including (1) instrument characterization and detector gain optimization, (2) antibody titration and sample staining, and (3) data acquisition and quality checks. This standardized approach has been applied to a variety of donors for a better understanding of the complexity of the human immune system.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00092-9.

Highly efficient reprogrammable mouse lines with integrated reporters to track the route to pluripotency

Revealing the molecular events associated with reprogramming different somatic cell types to pluripotency is critical for understanding the characteristics of induced pluripotent stem cell (iPSC) therapeutic derivatives. Inducible reprogramming factor transgenic cells or animals-designated as secondary (2°) reprogramming systems-not only provide excellent experimental tools for such studies but also offer a strategy to study the variances in cellular reprogramming outcomes due to different in vitro and in vivo environments. To make such studies less cumbersome, it is desirable to have a variety of efficient reprogrammable mouse systems to induce successful mass reprogramming in somatic cell types. Here, we report the development of two transgenic mouse lines from which 2° cells reprogram with unprecedented efficiency. These systems were derived by exposing primary reprogramming cells containing doxycycline-inducible Yamanaka factor expression to a transient interruption in transgene expression, resulting in selection for a subset of clones with robust transgene response. These systems also include reporter genes enabling easy readout of endogenous Oct4 activation (GFP), indicative of pluripotency, and reprogramming transgene expression (mCherry). Notably, somatic cells derived from various fetal and adult tissues from these 2° mouse lines gave rise to highly efficient and rapid reprogramming, with transgene-independent iPSC colonies emerging as early as 1 wk after induction. These mouse lines serve as a powerful tool to explore sources of variability in reprogramming and the mechanistic underpinnings of efficient reprogramming systems.

Elevated CD47 is a hallmark of dysfunctional aged muscle stem cells that can be targeted to augment regeneration

In aging, skeletal muscle strength and regenerative capacity decline, due in part to functional impairment of muscle stem cells (MuSCs), yet the underlying mechanisms remain elusive. Here, we capitalize on mass cytometry to identify high CD47 expression as a hallmark of dysfunctional MuSCs (CD47hi) with impaired regenerative capacity that predominate with aging. The prevalent CD47hi MuSC subset suppresses the residual functional CD47lo MuSC subset through a paracrine signaling loop, leading to impaired proliferation. We uncover that elevated CD47 levels on aged MuSCs result from increased U1 snRNA expression, which disrupts alternative polyadenylation. The deficit in aged MuSC function in regeneration can be overcome either by morpholino-mediated blockade of CD47 alternative polyadenylation or antibody blockade of thrombospondin-1/CD47 signaling, leading to improved regeneration in aged mice, with therapeutic implications. Our findings highlight a previously unrecognized age-dependent alteration in CD47 levels and function in MuSCs, which underlies reduced muscle repair in aging.

A developmental atlas of somatosensory diversification and maturation in the dorsal root ganglia by single-cell mass cytometry

Precisely controlled development of the somatosensory system is essential for detecting pain, itch, temperature, mechanical touch and body position. To investigate the protein-level changes that occur during somatosensory development, we performed single-cell mass cytometry on dorsal root ganglia from C57/BL6 mice of both sexes, with litter replicates collected daily from embryonic day 11.5 to postnatal day 4. Measuring nearly 3 million cells, we quantified 30 molecularly distinct somatosensory glial and 41 distinct neuronal states across all timepoints. Analysis of differentiation trajectories revealed rare cells that co-express two or more Trk receptors and over-express stem cell markers, suggesting that these neurotrophic factor receptors play a role in cell fate specification. Comparison to previous RNA-based studies identified substantial differences between many protein-mRNA pairs, demonstrating the importance of protein-level measurements to identify functional cell states. Overall, this study demonstrates that mass cytometry is a high-throughput, scalable platform to rapidly phenotype somatosensory tissues.

Acceleration of Mesenchymal-to-Epithelial Transition (MET) during Direct Reprogramming Using Natural Compounds

Induced pluripotent stem cells (iPSCs) can be generated from somatic cells using Oct4, Sox2, Klf4, and c-Myc (OSKM). Small molecules can enhance reprogramming. Licochalcone D (LCD), a flavonoid compound present mainly in the roots of Glycyrrhiza inflata, acts on known signaling pathways involved in transcriptional activity and signal transduction, including the PGC1-α and MAPK families. In this study, we demonstrated that LCD improved reprogramming efficiency. LCD-treated iPSCs (LCD-iPSCs) expressed pluripotency-related genes Oct4, Sox2, Nanog, and Prdm14. Moreover, LCD-iPSCs differentiated into all three germ layers in vitro and formed chimeras. The mesenchymal-to-epithelial transition (MET) is critical for somatic cell reprogramming. We found that the expression levels of mesenchymal genes (Snail2 and Twist) decreased and those of epithelial genes (DSP, Cldn3, Crb3, and Ocln) dramatically increased in OR-MEF (OG2^+/+/ROSA26^+/+) cells treated with LCD for 3 days, indicating that MET effectively occurred in LCD-treated OR-MEF cells. Thus, LCD enhanced the generation of iPSCs from somatic cells by promoting MET at the early stages of reprogramming.

Innate type 2 immunity controls hair follicle commensalism by Demodex mites

Demodex mites are commensal parasites of hair follicles (HFs). Normally asymptomatic, inflammatory outgrowth of mites can accompany malnutrition, immune dysfunction, and aging, but mechanisms restricting Demodex outgrowth are not defined. Here, we show that control of mite HF colonization in mice required group 2 innate lymphoid cells (ILC2s), interleukin-13 (IL-13), and its receptor, IL-4Ra-IL-13Ra1. HF-associated ILC2s elaborated IL-13 that attenuated HFs and epithelial proliferation at anagen onset; in their absence, Demodex colonization led to increased epithelial proliferation and replacement of gene programs for repair by aberrant inflammation, leading to the loss of barrier function and HF exhaustion. Humans with rhinophymatous acne rosacea, an inflammatory condition associated with Demodex, had increased HF inflammation with decreased type 2 cytokines, consistent with the inverse relationship seen in mice. Our studies uncover a key role for skin ILC2s and IL-13, which comprise an immune checkpoint that sustains cutaneous integrity and restricts pathologic infestation by colonizing HF mites.

Comparative roadmaps of reprogramming and oncogenic transformation identify Bcl11b and Atoh8 as broad regulators of cellular plasticity

Coordinated changes of cellular plasticity and identity are critical for pluripotent reprogramming and oncogenic transformation. However, the sequences of events that orchestrate these intermingled modifications have never been comparatively dissected. Here, we deconvolute the cellular trajectories of reprogramming (via Oct4/Sox2/Klf4/c-Myc) and transformation (via Ras/c-Myc) at the single-cell resolution and reveal how the two processes intersect before they bifurcate. This approach led us to identify the transcription factor Bcl11b as a broad-range regulator of cell fate changes, as well as a pertinent marker to capture early cellular intermediates that emerge simultaneously during reprogramming and transformation. Multiomics characterization of these intermediates unveiled a c-Myc/Atoh8/Sfrp1 regulatory axis that constrains reprogramming, transformation and transdifferentiation. Mechanistically, we found that Atoh8 restrains cellular plasticity, independent of cellular identity, by binding a specific enhancer network. This study provides insights into the partitioned control of cellular plasticity and identity for both regenerative and cancer biology.

Huyghe, Furlan et al. compare pluripotent reprogramming with oncogenic transformation and identify Bcl11b and Atoh8 as regulators of cellular plasticity in both processes, thus offering a unifying theory on the factors constraining cell fate changes.

P21-activated kinase 4 Pak4 maintains embryonic stem cell pluripotency via Akt activation

Exploiting the pluripotent properties of embryonic stem cells (ESCs) holds great promise for regenerative medicine. Nevertheless, directing ESC differentiation into specialized cell lineages requires intricate control governed by both intrinsic and extrinsic factors along with the actions of specific signaling networks. Here, we reveal the involvement of the p21-activated kinase 4 (Pak4), a serine/threonine kinase, in sustaining murine ESC (mESC) pluripotency. Pak4 is highly expressed in R1 ESC cells compared with embryonic fibroblast cells and its expression is progressively decreased during differentiation. Manipulations using knockdown and overexpression demonstrated a positive relationship between Pak4 expression and the clonogenic potential of mESCs. Moreover, ectopic Pak4 expression increases reprogramming efficiency of Oct4-Klf4-Sox2-Myc-induced pluripotent stem cells (iPSCs) whereas Pak4-knockdown iPSCs were largely incapable of generating teratomas containing mesodermal, ectodermal and endodermal tissues, indicative of a failure in differentiation. We further establish that Pak4 expression in mESCs is transcriptionally driven by the core pluripotency factor Nanog which recognizes specific binding motifs in the Pak4 proximal promoter region. In turn, the increased levels of Pak4 in mESCs fundamentally act as an upstream activator of the Akt pathway. Pak4 directly binds to and phosphorylates Akt at Ser473 with the resulting Akt activation shown to attenuate downstream GSK3β signaling. Thus, our findings indicate that the Nanog-Pak4-Akt signaling axis is essential for maintaining mESC self-renewal potential with further importance shown during somatic cell reprogramming where Pak4 appears indispensable for multi-lineage specification.

Dynamical landscapes of cell fate decisions

The generation of cellular diversity during development involves differentiating cells transitioning between discrete cell states. In the 1940s, the developmental biologist Conrad Waddington introduced a landscape metaphor to describe this process. The developmental path of a cell was pictured as a ball rolling through a terrain of branching valleys with cell fate decisions represented by the branch points at which the ball decides between one of two available valleys. Here we discuss progress in constructing quantitative dynamical models inspired by this view of cellular differentiation. We describe a framework based on catastrophe theory and dynamical systems methods that provides the foundations for quantitative geometric models of cellular differentiation. These models can be fit to experimental data and used to make quantitative predictions about cellular differentiation. The theory indicates that cell fate decisions can be described by a small number of decision structures, such that there are only two distinct ways in which cells make a binary choice between one of two fates. We discuss the biological relevance of these mechanisms and suggest the approach is broadly applicable for the quantitative analysis of differentiation dynamics and for determining principles of developmental decisions.

Single-cell proteomics defines the cellular heterogeneity of localized prostate cancer

Localized prostate cancer exhibits multiple genomic alterations and heterogeneity at the proteomic level. Single-cell technologies capture important cell-to-cell variability responsible for heterogeneity in biomarker expression that may be overlooked when molecular alterations are based on bulk tissue samples. This study aims to identify prognostic biomarkers and describe the heterogeneity of prostate cancer and the associated microenvironment by simultaneously quantifying 36 proteins using single-cell mass cytometry analysis of over 1.6 million cells from 58 men with localized prostate cancer. We perform this task, using a high-dimensional clustering pipeline named Franken to describe subpopulations of immune, stromal, and prostate cells, including changes occurring in tumor tissues and high-grade disease that provide insights into the coordinated progression of prostate cancer. Our results further indicate that men with localized disease already harbor rare subpopulations that typically occur in castration-resistant and metastatic disease.

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Single-cell proteomics of localized prostate cancer defines disease heterogeneity

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Malignant and benign prostate tissues differ in rare cell-type proportional shifts

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T cells and proliferating macrophages are associated with high-grade PCa

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Rare CD15⁺ epithelial cells are amplified in high-grade PCa

Label-Free Imaging to Track Reprogramming of Human Somatic Cells

The process of reprogramming patient samples to human-induced pluripotent stem cells (iPSCs) is stochastic, asynchronous, and inefficient, leading to a heterogeneous population of cells. In this study, we track the reprogramming status of patient-derived erythroid progenitor cells (EPCs) at the single-cell level during reprogramming with label-free live-cell imaging of cellular metabolism and nuclear morphometry to identify high-quality iPSCs. EPCs isolated from human peripheral blood of three donors were used for our proof-of-principle study. We found distinct patterns of autofluorescence lifetime for the reduced form of nicotinamide adenine dinucleotide (phosphate) and flavin adenine dinucleotide during reprogramming. Random forest models classified iPSCs with ∼95% accuracy, which enabled the successful isolation of iPSC lines from reprogramming cultures. Reprogramming trajectories resolved at the single-cell level indicated significant reprogramming heterogeneity along different branches of cell states. This combination of micropatterning, autofluorescence imaging, and machine learning provides a unique, real-time, and nondestructive method to assess the quality of iPSCs in a biomanufacturing process, which could have downstream impacts in regenerative medicine, cell/gene therapy, and disease modeling.

Revealing new biology from multiplexed, metal-isotope-tagged, single-cell readouts

Mass cytometry (MC) is a recent technology that pairs plasma-based ionization of cells in suspension with time-of-flight (TOF) mass spectrometry to sensitively quantify the single-cell abundance of metal-isotope-tagged affinity reagents to key proteins, RNA, and peptides. Given the ability to multiplex readouts (~50 per cell) and capture millions of cells per experiment, MC offers a robust way to assay rare, transitional cell states that are pertinent to human development and disease. Here, we review MC approaches that let us probe the dynamics of cellular regulation across multiple conditions and sample types in a single experiment. Additionally, we discuss current limitations and future extensions of MC as well as computational tools commonly used to extract biological insight from single-cell proteomic datasets.

Single-Cell Profiling of the Intrinsic Apoptotic Pathway by Mass Cytometry (CyTOF)

Mass cytometry time-of-flight (CyTOF) is a technology for the study of complex biological processes at the single-cell level. The technology enables measurement of >50 protein moieties on the surface and inside the cell. The power of CyTOF lies in the application of purpose-built panels of antibody probes that resolve features of key biological processes in a cell. Here, we describe this technology's use to profile changes in the intrinsic apoptotic (cell death) protein machinery at a single-cell level. We provide a comprehensive overview of a tailor-made set of cell survival/death antibodies, ideal staining conditions, and high-dimensional data analysis.

Robotic high-throughput biomanufacturing and functional differentiation of human pluripotent stem cells

Efficient translation of human induced pluripotent stem cells (hiPSCs) requires scalable cell manufacturing strategies for optimal self-renewal and functional differentiation. Traditional manual cell culture is variable and labor intensive, posing challenges for high-throughput applications. Here, we established a robotic platform and automated all essential steps of hiPSC culture and differentiation under chemically defined conditions. This approach allowed rapid and standardized manufacturing of billions of hiPSCs that can be produced in parallel from up to 90 different patient- and disease-specific cell lines. Moreover, we established automated multi-lineage differentiation and generated functional neurons, cardiomyocytes, and hepatocytes. To validate our approach, we compared robotic and manual cell culture operations and performed comprehensive molecular and cellular characterizations (e.g., single-cell transcriptomics, mass cytometry, metabolism, electrophysiology) to benchmark industrial-scale cell culture operations toward building an integrated platform for efficient cell manufacturing for disease modeling, drug screening, and cell therapy.

Multi-omics reveals clinically relevant proliferative drive associated with mTOR-MYC-OXPHOS activity in chronic lymphocytic leukemia

Chronic Lymphocytic Leukemia (CLL) has a complex pattern of driver mutations and much of its clinical diversity remains unexplained. We devised a method for simultaneous subgroup discovery across multiple data types and applied it to genomic, transcriptomic, DNA methylation and ex-vivo drug response data from 217 Chronic Lymphocytic Leukemia (CLL) cases. We uncovered a biological axis of heterogeneity strongly associated with clinical behavior and orthogonal to the known biomarkers. We validated its presence and clinical relevance in four independent cohorts (n=547 patients). We find that this axis captures the proliferative drive (PD) of CLL cells, as it associates with lymphocyte doubling rate, global hypomethylation, accumulation of driver aberrations and response to pro-proliferative stimuli. CLL-PD was linked to the activation of mTOR-MYC-oxidative phosphorylation (OXPHOS) through transcriptomic, proteomic and single cell resolution analysis. CLL-PD is a key determinant of disease outcome in CLL. Our multi-table integration approach may be applicable to other tumors whose inter-individual differences are currently unexplained.

The transcription factor code in iPSC reprogramming

Transcription factor (TF)-induced reprogramming of somatic cells across lineages and to induced pluripotent stem cells (iPSCs) has revealed a remarkable plasticity of differentiated cells and presents great opportunities for generating clinically relevant cell types for disease modeling and regenerative medicine. The understanding of iPSC reprogramming provides insights into the mechanisms that safeguard somatic cell identity, drive epigenetic reprogramming, and underlie cell fate specification in vivo. The combinatorial action of TFs has emerged as the key mechanism for the direct and indirect effects of reprogramming factors that induce the remodelling of the enhancer landscape. The interplay of TFs in iPSC reprogramming also yields trophectoderm- and extraembryonic endoderm-like cell populations, uncovering an intriguing plasticity of cell states and opening new avenues for exploring cell fate decisions during early embryogenesis.

A human mutation in STAT3 promotes type 1 diabetes through a defect in CD8+ T cell tolerance

Naturally occurring cases of monogenic type 1 diabetes (T1D) help establish direct mechanisms driving this complex autoimmune disease. A recently identified de novo germline gain-of-function (GOF) mutation in the transcriptional regulator STAT3 was found to cause neonatal T1D. We engineered a novel knock-in mouse incorporating this highly diabetogenic human STAT3 mutation (K392R) and found that these mice recapitulated the human autoimmune diabetes phenotype. Paired single-cell TCR and RNA sequencing revealed that STAT3-GOF drives proliferation and clonal expansion of effector CD8+ cells that resist terminal exhaustion. Single-cell ATAC-seq showed that these effector T cells are epigenetically distinct and have differential chromatin architecture induced by STAT3-GOF. Analysis of islet TCR clonotypes revealed a CD8+ cell reacting against known antigen IGRP, and STAT3-GOF in an IGRP-reactive TCR transgenic model demonstrated that STAT3-GOF intrinsic to CD8+ cells is sufficient to accelerate diabetes onset. Altogether, these findings reveal a diabetogenic CD8+ T cell response that is restrained in the presence of normal STAT3 activity and drives diabetes pathogenesis.

Single-cell analysis by mass cytometry reveals metabolic states of early-activated CD8+ T cells during the primary immune response

Memory T cells are thought to rely on oxidative phosphorylation and short-lived effector T cells on glycolysis. Here, we investigated how T cells arrive at these states during an immune response. To understand the metabolic state of rare, early-activated T cells, we adapted mass cytometry to quantify metabolic regulators at single-cell resolution in parallel with cell signaling, proliferation, and effector function. We interrogated CD8+ T cell activation in vitro and in response to Listeria monocytogenes infection in vivo. This approach revealed a distinct metabolic state in early-activated T cells characterized by maximal expression of glycolytic and oxidative metabolic proteins. Cells in this transient state were most abundant 5 days post-infection before rapidly decreasing metabolic protein expression. Analogous findings were observed in chimeric antigen receptor (CAR) T cells interrogated longitudinally in advanced lymphoma patients. Our study demonstrates the utility of single-cell metabolic analysis by mass cytometry to identify metabolic adaptations of immune cell populations in vivo and provides a resource for investigations of metabolic regulation of immune responses across a variety of applications.

The frontier of live tissue imaging across space and time

What you see is what you get-imaging techniques have long been essential for visualization and understanding of tissue development, homeostasis, and regeneration, which are driven by stem cell self-renewal and differentiation. Advances in molecular and tissue modeling techniques in the last decade are providing new imaging modalities to explore tissue heterogeneity and plasticity. Here we describe current state-of-the-art imaging modalities for tissue research at multiple scales, with a focus on explaining key tradeoffs such as spatial resolution, penetration depth, capture time/frequency, and moieties. We explore emerging tissue modeling and molecular tools that improve resolution, specificity, and throughput.

Current trends in flow cytometry automated data analysis software

Automated flow cytometry (FC) data analysis tools for cell population identification and characterization are increasingly being used in academic, biotechnology, pharmaceutical, and clinical laboratories. The development of these computational methods is designed to overcome reproducibility and process bottleneck issues in manual gating, however, the take-up of these tools remains (anecdotally) low. Here, we performed a comprehensive literature survey of state-of-the-art computational tools typically published by research, clinical, and biomanufacturing laboratories for automated FC data analysis and identified popular tools based on literature citation counts. Dimensionality reduction methods ranked highly, such as generic t-distributed stochastic neighbor embedding (t-SNE) and its initial Matlab-based implementation for cytometry data viSNE. Software with graphical user interfaces also ranked highly, including PhenoGraph, SPADE1, FlowSOM, and Citrus, with unsupervised learning methods outnumbering supervised learning methods, and algorithm type popularity spread across K-Means, hierarchical, density-based, model-based, and other classes of clustering algorithms. Additionally, to illustrate the actual use typically within clinical spaces alongside frequent citations, a survey issued by UK NEQAS Leucocyte Immunophenotyping to identify software usage trends among clinical laboratories was completed. The survey revealed 53% of laboratories have not yet taken up automated cell population identification methods, though among those that have, Infinicyt software is the most frequently identified. Survey respondents considered data output quality to be the most important factor when using automated FC data analysis software, followed by software speed and level of technical support. This review found differences in software usage between biomedical institutions, with tools for discovery, data exploration, and visualization more popular in academia, whereas automated tools for specialized targeted analysis that apply supervised learning methods were more used in clinical settings.

Flow cytometry: principles, applications and recent advances

Flow cytometry (FCM) is a sophisticated technique that works on the principle of light scattering and fluorescence emission by the specific fluorescent probe-labeled cells as they pass through a laser beam. It offers several unique advantages as it allows fast, relatively quantitative, multiparametric analysis of cell populations at the single cell level. In addition, it also enables physical sorting of the cells to separate the subpopulations based on different parameters. In this constantly evolving field, innovative technologies such as imaging FCM, mass cytometry and Raman FCM are being developed in order to address limitations of traditional FCM. This review explains the general principles, main applications and recent advances in the field of FCM.

CD73, Tumor Plasticity and Immune Evasion in Solid Cancers

Simple Summary

Tumors are ecosystems composed of cancer cells and non-tumor stroma together in a hypoxic environment often described as wounds that do not heal. Accumulating data suggest that solid tumors hijack cellular plasticity possibly to evade detection by the immune system. CD73-mediated generation of the purine nucleoside adenosine, is an important biochemical constituent of the immunosuppressive tumor microenvironment. In this review, the association between CD73 expression and features associated with cellular plasticity involving stemness, epithelial-to-mesenchymal transition and metastasis together with immune infiltration is summarized for a wide range of solid tumor types. Our analyses demonstrate that CD73 correlates with signatures associated with cellular plasticity in solid tumors. In addition, there are strong associations between CD73 expression and type of infiltrating lymphocytes. Collectively, the observations suggest a biomarker-based stratification to identify CD73-adenosinergic rich tumors may help identify patients with solid cancers who will respond to a combinatorial strategy that includes targeting CD73.

Abstract

Regulatory networks controlling cellular plasticity, important during early development, can re-emerge after tissue injury and premalignant transformation. One such regulatory molecule is the cell surface ectoenzyme ecto-5′-nucleotidase that hydrolyzes the conversion of extracellular adenosine monophosphate to adenosine (eADO). Ecto-5′-nucleotidase (NT5E) or cluster of differentiation 73 (CD73), is an enzyme that is encoded by NT5E in humans. In normal tissue, CD73-mediated generation of eADO has important pleiotropic functions ranging from the promotion of cell growth and survival, to potent immunosuppression mediated through purinergic G protein-coupled adenosine receptors. Importantly, tumors also utilize several mechanisms mediated by CD73 to resist therapeutics and in particular, evade the host immune system, leading to undesired resistance to targeted therapy and immunotherapy. Tumor cell CD73 upregulation is associated with worse clinical outcomes in a variety of cancers. Emerging evidence indicates a link between tumor cell stemness with a limited host anti-tumor immune response. In this review, we provide an overview of a growing body of evidence supporting the pro-tumorigenic role of CD73 and adenosine signaling. We also discuss data that support a link between CD73 expression and tumor plasticity, contributing to dissemination as well as treatment resistance. Collectively, targeting CD73 may represent a novel treatment approach for solid cancers.

ImaCytE: Visual Exploration of Cellular Micro-Environments for Imaging Mass Cytometry Data

Tissue functionality is determined by the characteristics of tissue-resident cells and their interactions within their microenvironment. Imaging Mass Cytometry offers the opportunity to distinguish cell types with high precision and link them to their spatial location in intact tissues at sub-cellular resolution. This technology produces large amounts of spatially-resolved high-dimensional data, which constitutes a serious challenge for the data analysis. We present an interactive visual analysis workflow for the end-to-end analysis of Imaging Mass Cytometry data that was developed in close collaboration with domain expert partners. We implemented the presented workflow in an interactive visual analysis tool; ImaCytE. Our workflow is designed to allow the user to discriminate cell types according to their protein expression profiles and analyze their cellular microenvironments, aiding in the formulation or verification of hypotheses on tissue architecture and function. Finally, we show the effectiveness of our workflow and ImaCytE through a case study performed by a collaborating specialist.

Single Cell Detection of the p53 Protein by Mass Cytometry

Simple Summary

Investigation of protein expression in cancer cells is an important part of the diagnostic process. Increasing knowledge about expression of different proteins has been exploited for prognostic assessments and in some cases also for selection of treatment. The p53 protein has proven important in development of various cancers, and the expression of this protein and its signaling pathway is therefore of interest when examining cancer patient samples. Here, we present mass cytometry as a tool for detection of p53 expression. Mass cytometry allows for measurement of up to 50 parameters per sample with single cell resolution, and we aim to demonstrate its potential for p53-focused research.

Abstract

Purpose: The p53 protein and its post-translational modifications are distinctly expressed in various normal cell types and malignant cells and are usually detected by immunohistochemistry and flow cytometry in contemporary diagnostics. Here, we describe an approach for simultaneous multiparameter detection of p53, its post-translational modifications and p53 pathway-related signaling proteins in single cells using mass cytometry. Method: We conjugated p53-specific antibodies to metal tags for detection by mass cytometry, allowing the detection of proteins and their post-translational modifications in single cells. We provide an overview of the antibody validation process using relevant biological controls, including cell lines treated in vitro with a stimulus (irradiation) known to induce changes in the expression level of p53. Finally, we present the potential of the method through investigation of primary samples from leukemia patients with distinct TP53 mutational status. Results: The p53 protein can be detected in cell lines and in primary samples by mass cytometry. By combining antibodies for p53-related signaling proteins with a surface marker panel, we show that mass cytometry can be used to decipher the single cell p53 signaling pathway in heterogeneous patient samples. Conclusion: Single cell profiling by mass cytometry allows the investigation of the p53 functionality through examination of relevant downstream signaling proteins in normal and malignant cells. Our work illustrates a novel approach for single cell profiling of p53.

Examining the Characteristics and Applications of Mesenchymal, Induced Pluripotent, and Embryonic Stem Cells for Tissue Engineering Approaches across the Germ Layers

The field of regenerative medicine utilizes a wide array of technologies and techniques for repairing and restoring function to damaged tissues. Among these, stem cells offer one of the most potent and promising biological tools to facilitate such goals. Implementation of mesenchymal stem cells (MSCs), induced pluripotent stem cells (iPSCs), and embryonic stem cells (ESCs) offer varying advantages based on availability and efficacy in the target tissue. The focus of this review is to discuss characteristics of these three subset stem cell populations and examine their utility in tissue engineering. In particular, the development of therapeutics that utilize cell-based approaches, divided by germinal layer to further assess research targeting specific tissues of the mesoderm, ectoderm, and endoderm. The combinatorial application of MSCs, iPSCs, and ESCs with natural and synthetic scaffold technologies can enhance the reparative capacity and survival of implanted cells. Continued efforts to generate more standardized approaches for these cells may provide improved study-to-study variations on implementation, thereby increasing the clinical translatability of cell-based therapeutics. Coupling clinically translatable research with commercially oriented methods offers the potential to drastically advance medical treatments for multiple diseases and injuries, improving the quality of life for many individuals.

The prospects of single-cell analysis in autoimmunity

In the last decade, there has been a tremendous development of technologies focused on analysing various molecular attributes in single cells, with an ever-increasing number of parameters becoming available at the DNA, RNA and protein levels. Much of this progress has involved cells in suspension, but also in situ analysis of tissues has taken great leaps. Paralleling the development in the laboratory, and because of increasing complexity, the analysis of single-cell data is also constantly being updated with new algorithms and analysis platforms. Our immune system shares this complexity, and immunologists have therefore been in the forefront of this technological development. These technologies clearly open new avenues for immunology research, maybe particularly within autoimmunity where the interaction between the faulty immune system and the thymus or the target organ is important. However, the technologies currently available can seem overwhelming and daunting. The aim of this review is to remedy this by giving a balanced overview of the prospects of using single-cell analysis in basal and clinical autoimmunity research, with an emphasis on endocrine autoimmunity.

Robotic High-Throughput Biomanufacturing and Functional Differentiation of Human Pluripotent Stem Cells

Efficient translation of human induced pluripotent stem cells (hiPSCs) depends on implementing scalable cell manufacturing strategies that ensure optimal self-renewal and functional differentiation. Currently, manual culture of hiPSCs is highly variable and labor-intensive posing significant challenges for high-throughput applications. Here, we established a robotic platform and automated all essential steps of hiPSC culture and differentiation under chemically defined conditions. This streamlined approach allowed rapid and standardized manufacturing of billions of hiPSCs that can be produced in parallel from up to 90 different patient-and disease-specific cell lines. Moreover, we established automated multi-lineage differentiation to generate primary embryonic germ layers and more mature phenotypes such as neurons, cardiomyocytes, and hepatocytes. To validate our approach, we carefully compared robotic and manual cell culture and performed molecular and functional cell characterizations (e.g. bulk culture and single-cell transcriptomics, mass cytometry, metabolism, electrophysiology, Zika virus experiments) in order to benchmark industrial-scale cell culture operations towards building an integrated platform for efficient cell manufacturing for disease modeling, drug screening, and cell therapy. Combining stem cell-based models and non-stop robotic cell culture may become a powerful strategy to increase scientific rigor and productivity, which are particularly important during public health emergencies (e.g. opioid crisis, COVID-19 pandemic).

EpCAM+CD73+ mark epithelial progenitor cells in postnatal human lung and are associated with pathogenesis of pulmonary disease including lung adenocarcinoma

Lung injury in mice induces mobilization of discrete subsets of epithelial progenitor cells to promote new airway and alveolar structures. However, whether similar cell types exist in human lung remains unresolved. Using flow cytometry, we identified a distinct cluster of cells expressing the epithelial cell adhesion molecule (EpCAM), a cell surface marker expressed on epithelial progenitor cells, enriched in the ecto-5'-nucleotidase CD73 in unaffected postnatal human lungs resected from pediatric patients with congenital lung lesions. Within the EpCAM⁺CD73⁺ population, a small subset coexpresses integrin β4 and HTII-280. This population remained stable with age. Spatially, EpCAM⁺CD73⁺ cells were positioned along the basal membrane of respiratory epithelium and alveolus next to CD73⁺ cells lacking EpCAM. Expanded EpCAM⁺CD73⁺ cells give rise to a pseudostratified epithelium in a two-dimensional air-liquid interface or a clonal three-dimensional organoid assay. Organoids generated under alveolar differentiation conditions were cystic-like and lacked robust alveolar mature cell types. Compared with unaffected postnatal lung, congenital lung lesions were marked by clusters of EpCAM⁺CD73⁺ cells in airway and cystic distal lung structures lined by simple epithelium composed of EpCAM⁺SCGB1A1⁺ cells and hyperplastic EpCAM⁺proSPC⁺ cells. In non-small-cell lung cancer (NSCLC), there was a marked increase in EpCAM⁺CD73⁺ tumor cells enriched in inhibitory immune checkpoint molecules CD47 and programmed death-ligand 1 (PD-L1), which was associated with poor survival in lung adenocarcinoma (LUAD). In conclusion, EpCAM⁺CD73⁺ cells are rare novel epithelial progenitor cells in the human lung. Importantly, reemergence of CD73 in lung adenocarcinoma enriched in negative immune checkpoint molecules may serve as a novel therapeutic target.

Defining Reprogramming Checkpoints from Single-Cell Analyses of Induced Pluripotency

Elucidating the mechanism of reprogramming is confounded by heterogeneity due to the low efficiency and differential kinetics of obtaining induced pluripotent stem cells (iPSCs) from somatic cells. Therefore, we increased the efficiency with a combination of epigenomic modifiers and signaling molecules and profiled the transcriptomes of individual reprogramming cells. Contrary to the established temporal order, somatic gene inactivation and upregulation of cell cycle, epithelial, and early pluripotency genes can be triggered independently such that any combination of these events can occur in single cells. Sustained co-expression of Epcam, Nanog, and Sox2 with other genes is required to progress toward iPSCs. Ehf, Phlda2, and translation initiation factor Eif4a1 play functional roles in robust iPSC generation. Using regulatory network analysis, we identify a critical role for signaling inhibition by 2i in repressing somatic expression and synergy between the epigenomic modifiers ascorbic acid and a Dot1L inhibitor for pluripotency gene activation.

Deep profiling of apoptotic pathways with mass cytometry identifies a synergistic drug combination for killing myeloma cells

Multiple myeloma is an incurable and fatal cancer of immunoglobulin-secreting plasma cells. Most conventional therapies aim to induce apoptosis in myeloma cells but resistance to these drugs often arises and drives relapse. In this study, we sought to identify the best adjunct targets to kill myeloma cells resistant to conventional therapies using deep profiling by mass cytometry (CyTOF). We validated probes to simultaneously detect 26 regulators of cell death, mitosis, cell signaling, and cancer-related pathways at the single-cell level following treatment of myeloma cells with dexamethasone or bortezomib. Time-resolved visualization algorithms and machine learning random forest models (RFMs) delineated putative cell death trajectories and a hierarchy of parameters that specified myeloma cell survival versus apoptosis following treatment. Among these parameters, increased amounts of phosphorylated cAMP response element-binding protein (CREB) and the pro-survival protein, MCL-1, were defining features of cells surviving drug treatment. Importantly, the RFM prediction that the combination of an MCL-1 inhibitor with dexamethasone would elicit potent, synergistic killing of myeloma cells was validated in other cell lines, in vivo preclinical models and primary myeloma samples from patients. Furthermore, CyTOF analysis of patient bone marrow cells clearly identified myeloma cells and their key cell survival features. This study demonstrates the utility of CyTOF profiling at the single-cell level to identify clinically relevant drug combinations and tracking of patient responses for future clinical trials.

Advances of Single-Cell Protein Analysis

Proteins play a significant role in the key activities of cells. Single-cell protein analysis provides crucial insights in studying cellular heterogeneities. However, the low abundance and enormous complexity of the proteome posit challenges in analyzing protein expressions at the single-cell level. This review summarizes recent advances of various approaches to single-cell protein analysis. We begin by discussing conventional characterization approaches, including fluorescence flow cytometry, mass cytometry, enzyme-linked immunospot assay, and capillary electrophoresis. We then detail the landmark advances of microfluidic approaches for analyzing single-cell protein expressions, including microfluidic fluorescent flow cytometry, droplet-based microfluidics, microwell-based assay (microengraving), microchamber-based assay (barcoding microchips), and single-cell Western blotting, among which the advantages and limitations are compared. Looking forward, we discuss future research opportunities and challenges for multiplexity, analyte, throughput, and sensitivity of the microfluidic approaches, which we believe will prompt the research of single-cell proteins such as the molecular mechanism of cell biology, as well as the clinical applications for tumor treatment and drug development.

Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line

The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge for understanding biological changes ranging from cellular differentiation to epigenetic responses of diseased cells upon drugging. We integrate experiments and theory to determine the trajectories that single BRAF^V600E mutant melanoma cancer cells take between drug-naive and drug-tolerant states. Although single-cell omics tools can yield snapshots of the cell-state landscape, the determination of individual cell trajectories through that space can be confounded by stochastic cell-state switching. We assayed for a panel of signaling, phenotypic, and metabolic regulators at points across 5 days of drug treatment to uncover a cell-state landscape with two paths connecting drug-naive and drug-tolerant states. The trajectory a given cell takes depends upon the drug-naive level of a lineage-restricted transcription factor. Each trajectory exhibits unique druggable susceptibilities, thus updating the paradigm of adaptive resistance development in an isogenic cell population.

Detailed understanding of how cancer cells transition from a drug sensitive to a tolerant state is lacking. Here, using single cell proteomic and metabolic data the authors uncover that isogenic BRAF mutant melanoma cells can take two distinct paths to become tolerant to BRAF inhibition.

Ultra-high throughput single-cell analysis of proteins and RNAs by split-pool synthesis

Single-cell omics provide insight into cellular heterogeneity and function. Recent technological advances have accelerated single-cell analyses, but workflows remain expensive and complex. We present a method enabling simultaneous, ultra-high throughput single-cell barcoding of millions of cells for targeted analysis of proteins and RNAs. Quantum barcoding (QBC) avoids isolation of single cells by building cell-specific oligo barcodes dynamically within each cell. With minimal instrumentation (four 96-well plates and a multichannel pipette), cell-specific codes are added to each tagged molecule within cells through sequential rounds of classical split-pool synthesis. Here we show the utility of this technology in mouse and human model systems for as many as 50 antibodies to targeted proteins and, separately, >70 targeted RNA regions. We demonstrate that this method can be applied to multi-modal protein and RNA analyses. It can be scaled by expansion of the split-pool process and effectively renders sequencing instruments as versatile multi-parameter flow cytometers.

Maeve O’Huallachain et al. report a method that enables simultaneous, ultra-high throughput single-cell barcoding for targeted single-cell protein and RNA analysis. They show the utility of their method in analyses of mRNA and protein expression in human and mouse cells.

Immune monitoring using mass cytometry and related high-dimensional imaging approaches

The cellular complexity and functional diversity of the human immune system necessitate the use of high-dimensional single-cell tools to uncover its role in multifaceted diseases such as rheumatic diseases, as well as other autoimmune and inflammatory disorders. Proteomic technologies that use elemental (heavy metal) reporter ions, such as mass cytometry (also known as CyTOF) and analogous high-dimensional imaging approaches (including multiplexed ion beam imaging (MIBI) and imaging mass cytometry (IMC)), have been developed from their low-dimensional counterparts, flow cytometry and immunohistochemistry, to meet this need. A growing number of studies have been published that use these technologies to identify functional biomarkers and therapeutic targets in rheumatic diseases, but the full potential of their application to rheumatic disease research has yet to be fulfilled. This Review introduces the underlying technologies for high-dimensional immune monitoring and discusses aspects necessary for their successful implementation, including study design principles, analytical tools and future developments for the field of rheumatology.

FLOW-MAP: a graph-based, force-directed layout algorithm for trajectory mapping in single-cell time course datasets

High-dimensional single-cell technologies present new opportunities for biological discovery, but the complex nature of the resulting datasets makes it challenging to perform comprehensive analysis. One particular challenge is the analysis of single-cell time course datasets: how to identify unique cell populations and track how they change across time points. To facilitate this analysis, we developed FLOW-MAP, a graphical user interface (GUI)-based software tool that uses graph layout analysis with sequential time ordering to visualize cellular trajectories in high-dimensional single-cell datasets obtained from flow cytometry, mass cytometry or single-cell RNA sequencing (scRNAseq) experiments. Here we provide a detailed description of the FLOW-MAP algorithm and how to use the open-source R package FLOWMAPR via its GUI or with text-based commands. This approach can be applied to many dynamic processes, including in vitro stem cell differentiation, in vivo development, oncogenesis, the emergence of drug resistance and cell signaling dynamics. To demonstrate our approach, we perform a step-by-step analysis of a single-cell mass cytometry time course dataset from mouse embryonic stem cells differentiating into the three germ layers: endoderm, mesoderm and ectoderm. In addition, we demonstrate FLOW-MAP analysis of a previously published scRNAseq dataset. Using both synthetic and experimental datasets for comparison, we perform FLOW-MAP analysis side by side with other single-cell analysis methods, to illustrate when it is advantageous to use the FLOW-MAP approach. The protocol takes between 30 min and 1.5 h to complete.

Dynamic distribution decomposition for single-cell snapshot time series identifies subpopulations and trajectories during iPSC reprogramming

Recent high-dimensional single-cell technologies such as mass cytometry are enabling time series experiments to monitor the temporal evolution of cell state distributions and to identify dynamically important cell states, such as fate decision states in differentiation. However, these technologies are destructive, and require analysis approaches that temporally map between cell state distributions across time points. Current approaches to approximate the single-cell time series as a dynamical system suffer from too restrictive assumptions about the type of kinetics, or link together pairs of sequential measurements in a discontinuous fashion. We propose Dynamic Distribution Decomposition (DDD), an operator approximation approach to infer a continuous distribution map between time points. On the basis of single-cell snapshot time series data, DDD approximates the continuous time Perron-Frobenius operator by means of a finite set of basis functions. This procedure can be interpreted as a continuous time Markov chain over a continuum of states. By only assuming a memoryless Markov (autonomous) process, the types of dynamics represented are more general than those represented by other common models, e.g., chemical reaction networks, stochastic differential equations. Furthermore, we can a posteriori check whether the autonomy assumptions are valid by calculation of prediction error-which we show gives a measure of autonomy within the studied system. The continuity and autonomy assumptions ensure that the same dynamical system maps between all time points, not arbitrarily changing at each time point. We demonstrate the ability of DDD to reconstruct dynamically important cell states and their transitions both on synthetic data, as well as on mass cytometry time series of iPSC reprogramming of a fibroblast system. We use DDD to find previously identified subpopulations of cells and to visualise differentiation trajectories. Dynamic Distribution Decomposition allows interpretation of high-dimensional snapshot time series data as a low-dimensional Markov process, thereby enabling an interpretable dynamics analysis for a variety of biological processes by means of identifying their dynamically important cell states.

Targeting cell plasticity for regeneration: From in vitro to in vivo reprogramming

The discovery of induced pluripotent stem cells (iPSCs), reprogrammed to pluripotency from somatic cells, has transformed the landscape of regenerative medicine, disease modelling and drug discovery pipelines. Since the first generation of iPSCs in 2006, there has been enormous effort to develop new methods that increase reprogramming efficiency, and obviate the need for viral vectors. In parallel to this, the promise of in vivo reprogramming to convert cells into a desired cell type to repair damage in the body, constitutes a new paradigm in approaches for tissue regeneration. This review article explores the current state of reprogramming techniques for iPSC generation with a specific focus on alternative methods that use biophysical and biochemical stimuli to reduce or eliminate exogenous factors, thereby overcoming the epigenetic barrier towards vector-free approaches with improved clinical viability. We then focus on application of iPSC for therapeutic approaches, by giving an overview of ongoing clinical trials using iPSCs for a variety of health conditions and discuss future scope for using materials and reagents to reprogram cells in the body.

Mass cytometry-based single-cell analysis of human stem cell reprogramming uncovers differential regulation of specific pluripotency markers

Human induced pluripotent stem cells (hiPSCs) are reprogrammed from somatic cells and are regarded as promising sources for regenerative medicine and disease research. Recently, techniques for analyses of individual cells, such as single-cell RNA-Seq and mass cytometry, have been used to understand the stem cell reprogramming process in the mouse. However, the reprogramming process in hiPSCs remains poorly understood. Here we used mass cytometry to analyze the expression of pluripotency and cell cycle markers in the reprogramming of human stem cells. We confirmed that, during reprogramming, the main cell population was shifted to an intermediate population consisting of neither fibroblasts nor hiPSCs. Detailed population analyses using computational approaches, including dimensional reduction by spanning-tree progression analysis of density-normalized events, PhenoGraph, and diffusion mapping, revealed several distinct cell clusters representing the cells along the reprogramming route. Interestingly, correlation analysis of various markers in hiPSCs revealed that the pluripotency marker TRA-1-60 behaves in a pattern that is different from other pluripotency markers. Furthermore, we found that the expression pattern of another pluripotency marker, octamer-binding protein 4 (OCT4), was distinctive in the pHistone-H3^high population (M phase) of the cell cycle. To the best of our knowledge, this is the first mass cytometry-based investigation of human reprogramming and pluripotency. Our analysis elucidates several aspects of hiPSC reprogramming, including several intermediate cell clusters active during the process of reprogramming and distinctive marker expression patterns in hiPSCs.

Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution

Elucidating the spectrum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises insights on cancer progression and drug resistance. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify, through TGFβ-withdrawal, a distinct MET state. We demonstrate significant differences between EMT and MET trajectories using a computational tool (TRACER) for reconstructing trajectories between cell states. In addition, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET PHENOtypic STAte MaP (PHENOSTAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET PHENOSTAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework to phenotypically characterize clinical samples in the context of in vitro EMT-MET findings which could help assess clinical relevance of EMT in cancer in future studies.

Intermediate transitions between epithelial and mesenchymal states are associated with tumor progression. Here using mass cytometry, Plevritis and colleagues develop a computational framework to resolve and map these trajectories in lung cancer cells and clinical specimens.

Visualizing structure and transitions in high-dimensional biological data

The high-dimensional data created by high-throughput technologies require visualization tools that reveal data structure and patterns in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure using an information-geometric distance between data points. We compare PHATE to other tools on a variety of artificial and biological datasets, and find that it consistently preserves a range of patterns in data, including continual progressions, branches and clusters, better than other tools. We define a manifold preservation metric, which we call denoised embedding manifold preservation (DEMaP), and show that PHATE produces lower-dimensional embeddings that are quantitatively better denoised as compared to existing visualization methods. An analysis of a newly generated single-cell RNA sequencing dataset on human germ-layer differentiation demonstrates how PHATE reveals unique biological insight into the main developmental branches, including identification of three previously undescribed subpopulations. We also show that PHATE is applicable to a wide variety of data types, including mass cytometry, single-cell RNA sequencing, Hi-C and gut microbiome data.

Excluding Oct4 from Yamanaka Cocktail Unleashes the Developmental Potential of iPSCs

Oct4 is widely considered the most important among the four Yamanaka reprogramming factors. Here, we show that the combination of Sox2, Klf4, and cMyc (SKM) suffices for reprogramming mouse somatic cells to induced pluripotent stem cells (iPSCs). Simultaneous induction of Sox2 and cMyc in fibroblasts triggers immediate retroviral silencing, which explains the discrepancy with previous studies that attempted but failed to generate iPSCs without Oct4 using retroviral vectors. SKM induction could partially activate the pluripotency network, even in Oct4-knockout fibroblasts. Importantly, reprogramming in the absence of exogenous Oct4 results in greatly improved developmental potential of iPSCs, determined by their ability to give rise to all-iPSC mice in the tetraploid complementation assay. Our data suggest that overexpression of Oct4 during reprogramming leads to off-target gene activation during reprogramming and epigenetic aberrations in resulting iPSCs and thereby bear major implications for further development and application of iPSC technology.

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SKM can induce pluripotency in somatic cells in the absence of exogenous Oct4

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SM coexpression activates the retroviral silencing machinery in somatic cells

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Oct4 overexpression drives massive off-target gene activation during reprogramming

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OSKM, but not SKM, iPSCs show abnormal imprinting and differentiation patterns

CytoML for cross-platform cytometry data sharing

CytoML is an R/Bioconductor package that enables cross‐platform import, export, and sharing of gated cytometry data. It currently supports Cytobank, FlowJo, Diva, and R, allowing users to import gated cytometry data from commercial platforms into R. Once data are available in R, the data can be further manipulated. For example it can be combined with other computational and analytic approaches, and the results can be exported to FlowJo or Cytobank to be explored by researchers using those platforms. We demonstrate how CytoML and related R packages can be used as a tool to import, modify and export several samples stained with the T cell panel from the FlowCAP IV Lyoplate data set. Once imported, the gating is modified using computational approaches, and exported for visualization in Cytobank and FlowJo. We further show how CytoML can be used to import gated data from a publicly accessible mass cytometry experiment from Cytobank. CytoML is the only tool that allows such sharing of gated cytometry data between researchers working across different platforms, and it will serve as a useful tool for validating and verifying the reproducibility of analyses. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Tracking and Predicting Human Somatic Cell Reprogramming Using Nuclear Characteristics

Reprogramming of human somatic cells to induced pluripotent stem cells (iPSCs) generates valuable resources for disease modeling, toxicology, cell therapy, and regenerative medicine. However, the reprogramming process can be stochastic and inefficient, creating many partially reprogrammed intermediates and non-reprogrammed cells in addition to fully reprogrammed iPSCs. Much of the work to identify, evaluate, and enrich for iPSCs during reprogramming relies on methods that fix, destroy, or singularize cell cultures, thereby disrupting each cell's microenvironment. Here, we develop a micropatterned substrate that allows for dynamic live-cell microscopy of hundreds of cell subpopulations undergoing reprogramming while preserving many of the biophysical and biochemical cues within the cells' microenvironment. On this substrate, we were able to both watch and physically confine cells into discrete islands during the reprogramming of human somatic cells from skin biopsies and blood draws obtained from healthy donors. Using high-content analysis, we identified a combination of eight nuclear characteristics that can be used to generate a computational model to predict the progression of reprogramming and distinguish partially reprogrammed cells from those that are fully reprogrammed. This approach to track reprogramming in situ using micropatterned substrates could aid in biomanufacturing of therapeutically relevant iPSCs and be used to elucidate multiscale cellular changes (cell-cell interactions as well as subcellular changes) that accompany human cell fate transitions.