Single-cell Dynamics Research Articles

Dynamic single cell analysis in a proximal-tubule-on-chip reveals heterogeneous epithelial colonization strategies of uropathogenic Escherichia coli under shear stress.

The urinary tract is a hydrodynamically challenging microenvironment and uropathogenic Escherichia coli (UPEC) must overcome several physiological challenges in order to adhere and establish a urinary tract infection. Our previous work in vivo revealed a synergy between different UPEC adhesion organelles, which facilitated effective colonization of the renal proximal tubule. To allow high-resolution real-time analysis of this colonization behavior, we established a biomimetic proximal-tubule-on-chip (PToC). The PToC allowed for single-cell resolution analysis of the first stages of bacterial interaction with host epithelial cells, under physiological flow. Time-lapse microscopy and single-cell trajectory analysis in the PToC revealed that while the majority of UPEC moved directly through the system, a minority population initiated heterogeneous adhesion, identified as either rolling or bound. Adhesion was predominantly transient and mediated by P pili at the earliest time-points. These bound bacteria initiated a founder population which rapidly divided, leading to 3D microcolonies. Within the first hours, the microcolonies did not express extracellular curli matrix, but rather were dependent on Type 1 fimbriae as the key element in the microcolony structure. Collectively, our results show the application of Organ-on-chip technology to address bacterial adhesion behaviors, demonstrating a well-orchestrated interplay and redundancy between adhesion organelles that enables UPEC to form microcolonies and persist under physiological shear stress.

Changes in interactions over ecological time scales influence single-cell growth dynamics in a metabolically coupled marine microbial community

Microbial communities thrive in almost all habitats on earth. Within these communities, cells interact through the release and uptake of metabolites. These interactions can have synergistic or antagonistic effects on individual community members. The collective metabolic activity of microbial communities leads to changes in their local environment. As the environment changes over time, the nature of the interactions between cells can change. We currently lack understanding of how such dynamic feedbacks affect the growth dynamics of individual microbes and of the community as a whole. Here we study how interactions mediated by the exchange of metabolites through the environment change over time within a simple marine microbial community. We used a microfluidic-based approach that allows us to disentangle the effect cells have on their environment from how they respond to their environment. We found that the interactions between two species—a degrader of chitin and a cross-feeder that consumes metabolic by-products—changes dynamically over time as cells modify their environment. Cells initially interact positively and then start to compete at later stages of growth. Our results demonstrate that interactions between microorganisms are not static and depend on the state of the environment, emphasizing the importance of disentangling how modifications of the environment affects species interactions. This experimental approach can shed new light on how interspecies interactions scale up to community level processes in natural environments.

Enhanced mechanical heterogeneity of cell collectives due to temporal fluctuations in cell elasticity.

Cells are dynamic systems characterized by temporal variations in biophysical properties such as stiffness and contractility. Recent studies show that the recruitment and release of actin filaments into and out of the cell cortex-a network of proteins underneath the cell membrane-leads to cell stiffening prior to division and softening immediately afterward. In three-dimensional (3D) cell collectives, it is unclear whether the stiffness change during division at the single-cell scale controls the spatial structure and dynamics at the multicellular scale. This is an important question to understand because cell stiffness variations impact cell spatial organization and cancer progression. Using a minimal 3D model incorporating cell birth, death, and cell-to-cell elastic and adhesive interactions, we investigate the effect of mechanical heterogeneity-variations in individual cell stiffnesses that make up the cell collective-on tumor spatial organization and cell dynamics. We discover that spatial mechanical heterogeneity characterized by a spheroid core composed of stiffer cells and softer cells in the periphery emerges within dense 3D cell collectives, which may be a general feature of multicellular tumor growth. We show that heightened spatial mechanical heterogeneity enhances single-cell dynamics and volumetric tumor growth driven by fluctuations in cell elasticity. Our results could have important implications in understanding how spatiotemporal variations in single-cell stiffness determine tumor growth and spread.

Beyond the average: An updated framework for understanding the relationship between cell growth, DNA replication, and division in a bacterial system.

Our understanding of the bacterial cell cycle is framed largely by population-based experiments that focus on the behavior of idealized average cells. Most famously, the contributions of Cooper and Helmstetter help to contextualize the phenomenon of overlapping replication cycles observed in rapidly growing bacteria. Despite the undeniable value of these approaches, their necessary reliance on the behavior of idealized average cells masks the stochasticity inherent in single-cell growth and physiology and limits their mechanistic value. To bridge this gap, we propose an updated and agnostic framework, informed by extant single-cell data, that quantitatively accounts for stochastic variations in single-cell dynamics and the impact of medium composition on cell growth and cell cycle progression. In this framework, stochastic timers sensitive to medium composition impact the relationship between cell cycle events, accounting for observed differences in the relationship between cell cycle events in slow- and fast-growing cells. We conclude with a roadmap for potential application of this framework to longstanding open questions in the bacterial cell cycle field.

Micropipette-assisted atomic force microscopy for single-cell 3D manipulations and nanomechanical measurements.

Mechanical cues play a crucial role in regulating physiological and pathological processes, and atomic force microscopy (AFM) has become an important and standard tool for measuring the mechanical properties of single cells. In particular, providing a capability to manipulate cells in a three-dimensional (3D) space benefits enhancing the applications of AFM measurements in cell biology. Here, we present the complementary integration of AFM and micropipette micromanipulation, which allows precise 3D manipulations and nanomechanical measurements of single living cells. A micropipette micromanipulation system under the guidance of optical microscopy was established to isolate single living cells, and polydimethylsiloxane (PDMS) micropillar substrates were used to physically immobilize the isolated living cells for downstream AFM detection. The viscoelastic properties (Young's modulus, relaxation time, viscosity) of cells were quantitatively measured by AFM-based indentation assay. The effectiveness of micropipette-assisted AFM in single-cell analysis was confirmed on both living animal suspended cells and living animal adherent cells, showing dramatic changes in cell mechanics in different states and revealing the dynamics of single cells grown on micropillar arrays. The study demonstrates the great potential of a micropipette to aid AFM in single-cell manipulations for better accessing the mechanical cues involved in cellular processes, which will allow additional studies of single-cell mechanics and will benefit the field of mechanobiology.

A Guide Toward Multi-scale and Quantitative Branching Analysis in the Mammary Gland.

The mammary gland consists of a bilayered epithelial structure with an extensively branched morphology. The majority of this epithelial tree is laid down during puberty, during which actively proliferating terminal end buds repeatedly elongate and bifurcate to form the basic structure of the ductal tree. Mammary ducts consist of a basal and luminal cell layer with a multitude of identified sub-lineages within both layers. The understanding of how these different cell lineages are cooperatively driving branching morphogenesis is a problem of crossing multiple scales, as this requires information on the macroscopic branched structure of the gland, as well as data on single-cell dynamics driving the morphogenic program. Here we describe a method to combine genetic lineage tracing with whole-gland branching analysis. Quantitative data on the global organ structure can be used to derive a model for mammary gland branching morphogenesis and provide a backbone on which the dynamics of individual cell lineages can be simulated and compared to lineage-tracing approaches. Eventually, these quantitative models and experiments allow to understand the couplings between the macroscopic shape of the mammary gland and the underlying single-cell dynamics driving branching morphogenesis.

Global Characteristics and Dynamics of Single Immune Cells After Myocardial Infarction.

Background Myocardial infarction (MI) is characterized by the emergence of dead or dying cardiomyocytes and excessive immune cell infiltration after coronary vessel occlusion. However, the complex transcriptional profile, pathways, cellular interactome, and transcriptional regulators of immune subpopulations after MI remain elusive. Methods and Results Here, male C57BL/6 mice were subjected to MI surgery and monitored for 1 day and 7 days, or sham surgery for 7 days, then cardiac CD45-positive immune cells were collected for single-cell RNA sequencing to determine immune heterogeneity. A total of 30 135 CD45+ immune cells were partitioned into macrophages, monocytes, neutrophils, dendritic cells, and T or B cells for further analysis. We showed that macrophages enriched for Olr1 and differentially expressed Gpnmb represented 2 crucial ischemia-associated macrophages with distinct proinflammatory and prophagocytic capabilities. In contrast to the proinflammatory subset of macrophages enriched for Olr1, Gpnmb-positive macrophages exhibited higher phagocytosis and fatty acid oxidation preference, which could be abolished by etomoxir treatment. In addition to macrophages, MI triggered prompt recruitment of neutrophils into murine hearts, which constituted the sequential cell-fate from naïve S100a4-positive, to activated Sell-high, to aging Icam1-high neutrophils. In silico tools predicted that the excessively expanded neutrophils at 1 day were attributed to chemokine C-C motif ligand/chemokine C-X-C motif ligand pathways, whereas CD80/inducible T-cell costimulator (ICOS) signaling was responsible for the immunosuppressive response at day 7 after MI. Finally, the Fos/AP-1 (activator protein 1) regulon was identified as the critical regulator of proinflammatory responses, which was significantly activated in patients with dilated cardiomyopathy and ischemic cardiomyopathy. We showed the enriched Fos/AP-1 target gene loci in genome-wide association study signals for coronary artery diseases and MI. Targeting Fos/AP-1 with the selective inhibitor T5224 blunted leukocyte infiltration and alleviated cardiac dysfunction in the preclinical murine MI model. Conclusions Taken together, this single-cell RNA sequencing data lay the groundwork for the understanding of immune cell heterogeneity and dynamics in murine ischemic hearts. Moreover, Fos/AP-1 inhibition mitigates inflammatory responses and cardiac dysfunction, which might provide potential therapeutic benefits for heart failure intervention after MI.

Bacto_tracker: a method for single-cell tracking of M. xanthus in dense and multispecies colonies

Cell motility and predation are important for the dynamics of many multi-cellular ecosystems, such as the gut or the soil. Approaches to image cell dynamics in such complex systems are scant, and high-throughput analysis methods to segment and track single-cell behaviors are currently lacking. Here, we addressed these limitations by implementing a fast fluorescence microscopy technique enabling the high-resolution acquisition of cell movement over large areas and long time periods. Next, we applied deep learning to semantically segment two different bacteria species within complex micro-environments . We implemented a method to build single cell traces by combining the cell masks from different time points to follow the dynamics of single cells with high spatial and temporal resolutions and over long periods of time. We applied and validated these methods by characterizing the dynamics of Escherichia coli predation by Myxococcus xanthus.

Droplet-Based Microfluidic Platform for High Spatiotemporal Resolved Single-Cell Signaling Profiling

A small indentation embedded in a microchannel creates a surface energy well (SEW) for a confined droplet due to surface energy release. Inspired by this, we developed a SEW-based microfluidic platform to realize high spatiotemporal-resolved signal profiling at the single-cell level applying droplet stimulus on a single chip. The method allows for controlled droplet replacement within only 3 s with almost 100% exchange efficiency, reliable single-cell patterning of adherent cells and successive treatment of adherent cells with reagent droplets. Furthermore, the PDGFR/Akt pathway served as a model system for evaluating the performance of the SEW-based method in determining the effects of ligand stimulation duration (3 s to 3 min) on receptor phosphorylation. The novel strategy offers a general platform for probing the temporal dynamics of single cells, as well for monitoring rapid chemical reactions in various applications.

DeepVelo: Single-cell transcriptomic deep velocity field learning with neural ordinary differential equations.

Recent advances in single-cell sequencing technologies have provided unprecedented opportunities to measure the gene expression profile and RNA velocity of individual cells. However, modeling transcriptional dynamics is computationally challenging because of the high-dimensional, sparse nature of the single-cell gene expression measurements and the nonlinear regulatory relationships. Here, we present DeepVelo, a neural network-based ordinary differential equation that can model complex transcriptome dynamics by describing continuous-time gene expression changes within individual cells. We apply DeepVelo to public datasets from different sequencing platforms to (i) formulate transcriptome dynamics on different time scales, (ii) measure the instability of cell states, and (iii) identify developmental driver genes via perturbation analysis. Benchmarking against the state-of-the-art methods shows that DeepVelo can learn a more accurate representation of the velocity field. Furthermore, our perturbation studies reveal that single-cell dynamical systems could exhibit chaotic properties. In summary, DeepVelo allows data-driven discoveries of differential equations that delineate single-cell transcriptome dynamics.

Persistence of Mycobacterium tuberculosis in response to infection burden and host-induced stressors.

As infection with Mycobacterium tuberculosis progresses, the bacilli experience various degrees of host stressors in the macrophage phagosome such as low pH, nutrient deprivation, or exposure to toxic agents, which promotes cell-to-cell phenotypic variation. This includes a physiologically viable but non- or slowly replicating persister subpopulation, which is characterised by a loss of growth on solid media, while remaining metabolically active. Persisters additionally evade the host immune response and macrophage antimicrobial processes by adapting their metabolic pathways to maintain survival and persistence in the host. A flow cytometry-based dual-fluorescent replication reporter assay, termed fluorescence dilution, provided a culture-independent method to characterize the single-cell replication dynamics of M. tuberculosis persisters following macrophage infection. Fluorescence dilution in combination with reference counting beads and a metabolic esterase reactive probe, calcein violet AM, provided an effective approach to enumerate and characterize the phenotypic heterogeneity within M. tuberculosis following macrophage infection. Persister formation appeared dependent on the initial infection burden and intracellular bacterial burden. However, inhibition of phagocytosis by cytochalasin D treatment resulted in a significantly higher median percentage of persisters compared to inhibition of phagosome acidification by bafilomycin A1 treatment. Our results suggest that different host factors differentially impact the intracellular bacterial burden, adaptive mechanisms and entry into persistence in macrophages.

SOTIP is a versatile method for microenvironment modeling with spatial omics data

The rapidly developing spatial omics generated datasets with diverse scales and modalities. However, most existing methods focus on modeling dynamics of single cells while ignore microenvironments (MEs). Here we present SOTIP (Spatial Omics mulTIPle-task analysis), a versatile method incorporating MEs and their interrelationships into a unified graph. Based on this graph, spatial heterogeneity quantification, spatial domain identification, differential microenvironment analysis, and other downstream tasks can be performed. We validate each module’s accuracy, robustness, scalability and interpretability on various spatial omics datasets. In two independent mouse cerebral cortex spatial transcriptomics datasets, we reveal a gradient spatial heterogeneity pattern strongly correlated with the cortical depth. In human triple-negative breast cancer spatial proteomics datasets, we identify molecular polarizations and MEs associated with different patient survivals. Overall, by modeling biologically explainable MEs, SOTIP outperforms state-of-art methods and provides some perspectives for spatial omics data exploration and interpretation.

Unveiling Highly Fluctuating Single-Cell Transcriptome Dynamics at a Novel Bifurcation Point in a Human Lymphoid Pathway

Background: Human hematopoiesis was considered a hierarchical stepwise process, but on the basis of recent progress in single-cell RNA-sequencing (scRNA-seq) it is now considered a continuous process. However, the uncertainty or fluctuation of single-cell transcriptome dynamics during differentiation was not considered. To address this issue, we developed a new computational methodology that converts single-cell splicing kinetics into an advection-diffusion model in a latent cell state using deep learning technology, which enabled us to quantitatively capture the fluctuation in cell state dynamics. Because the human lymphoid pathway and bifurcation points for dendritic cells (DC)/monocytic lineage remain incompletely defined, we applied our method to explore the lymph-DC pathway in the human lympho-hematopoietic system. Materials and methods: CD34+ or CD34+CD38+CD45RA+CD19-CD7-CD10+ cells (CD10SPs) were sorted from a mixture of frozen human cord blood samples, and scRNA-seq and single-cell chromatin accessibility analysis (scATAC) were performed. The data were applied to the variational inference of cell state dynamics with fluctuation (VICDYF). Subsequently, the obtained findings were validated by novel mesenchymal stem cell (MSC)-like coculture system that supports comprehensive human hematopoiesis. Results: MSC-like cultures of various lymphoid populations showed that the CD10SP population, which had been thought to be B/NK progenitors, was a heterogenous population in which lineage diversification from multi-lymphoid progenitors toward lymphoids and DCs was suspected to occur. Our insilico analysis of CD34+ cells with VICDYF suggested a B/plasmacytoid DC (pDC) bifurcation with high fluctuating transcriptome dynamics in the CD10-expressing fraction among various levels of fluctuating bifurcation points. Analysis of CD10SPs identified the B/pDC bifurcation as a highly fluctuating transcriptome state in the IL-7Rα+ fraction in association with the LFA-1 expression dynamics and pDC differentiation bias. Consistent with these findings, B/pDC common progenitors were identified by single cell culture, and the LFA-1-high population gave rise to a higher percentage of pDCs than proB cells, while the LFA-1-low population produced a higher percentage of proB cells than pDCs in the c-kit+IL-7Rα+ fraction. Of note, the epigenetic regulator SFMBT2 was specifically expressed in the B/pDC bifurcation region. The scATAC of the CD34+ population revealed that the B/pDC bifurcation with large fluctuation coincided with the high accessibility of both sides of the differentially accessible chromatin regions for both B and pDC lineages. This study revealed a new landscape of the human lymphoid pathway that includes crucial bifurcation points with highly fluctuating single-cell transcriptome dynamics. Discussion: Our computational methodology discovered a novel B/pDC bifurcation as high fluctuating dynamics. The insilico predictions were confirmed by the biological findings, using MSC-like cultures. Although the underlying molecular mechanism remains elusive, single-cell multiomics analysis suggests that the estimated large fluctuation is associated with epigenetic changes in chromatin regions for both B and pDC lineages. VICDYF also identified the highly fluctuated transcriptome dynamics at the putative lineage bifurcation point during pancreatic development. Conclusion: Our results provide a new model of differentiation - fluctuation based differentiation - which reconciles continuous and stepwise models. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Age-Related Immune Cell Dynamics Influences Outcomes after Allogeneic Hematopoietic Cell Transplantation

Allogeneic hematopoietic cell transplantation (HCT) holds a pivotal place in the setting of therapeutic algorithms for hematological disorders. Its therapeutic index is principally related to the immunogenic graft-versus-leukemia (GvL) effect that, together with an efficient immune reconstitution ensures the success of the procedure, contributing to disease control and immune competence. Factors influencing recipient immune recovery have been largely investigated across multiple cohorts issuing heterogenous results. Differences in outcomes in adult and pediatric populations suggest an age-related contribution to post-transplant immune reconstitution, however it is unclear how this parameter may affect the dynamics of single immune cells. Here we conducted a monocentric prospective study questioning on the modifications of immune cell recovery in a cohort of 176 patients (58 children and 118 adults) with hematological disorders receiving a HCT between 2015 and 2020 and followed at our institution. Complete flow-based immune reconstitution data on T-CD4, T-CD8, NK and B cell populations were collected at defined time-points (+1, 3, 6 and 12 months post-HCT). After ascertaining the dynamics in adult and pediatric populations of each cell component, the effect of a set of baseline clinico-biological variables on late (>6 months) reconstitution for each cell population was tested through univariable and multivariable linear or logistic regression models. First, we analyzed the linear relationship existing between recipient and donor age and each cell compartment. Interestingly, while donor age did not impact on any of the parameters in study, CD4- T (Adj R2 0.4, p=5.7x 10-15) and, in minor part, B cell post-transplant increments (Adj R2 0.05, p=0.004) were inversely correlated with recipient age, which instead did not influence CD8-T nor NK cell reconstitution. Results of stepwise multivariable analyses showed that CD8-T cell reconstitution was positively impacted by recipient positive CMV serostatus (p=0.00009) while donor CMV serostatus positively influenced CD4-T cell recovery (p=0.017). This effect was likely dependent from the expansion of CMV related T cell clonotypes in case of previous CMV immunity. The inverse correlation between CD4-T cells and recipient age was confirmed in multivariable analysis (p=0.0148). Donor EBV positive serostatus resulted instead to be a negative predictor of B cell recovery (p=0.0442). Interestingly none of the parameters analyzed influenced NK cell recovery. The impact on age mirrored a different kinetics of immune reconstitution in adult and pediatric patients (<18 years). Therefore, while early after HCT immune cell composition was similar in the two groups, with NK cells representing the largest component, followed by CD8-T and CD4-T, and minimal B cell contribution, in later phases we assisted to a faster expansion of CD4-T arm in children whereas CD8-T remained the major contributor of the immune recovery processes in adults still one year after transplant. B cell reconstitution was also faster in children, with a better immunoglobulin levels recuperation (measured on IgM to avoid bias of prophylactic IgG infusions) despite no differences in post-transplant pre-emptive rituximab utilization. When looking at different outcomes of pediatric and adult patients, both overall and event free survival were significantly higher in children (p=0.00062 and p=0.0062). While no difference in acute grade III-IV GvHD, adults experienced a significantly higher cumulative incidence (CI) of chronic GvHD (p=0.0258) and of specific infections, including EBV (p=0.031) and fungi (p=0.007). Although CI of relapse was not different, children experienced later relapses as compared to adults (median of 294 vs. 139 days, p=0.039), indicating a better disease control at least at early post-transplant stages. Here, starting from immune reconstitution data prospectively collected for patients transplanted at our institution, we dissected the effect of age on post-transplant immune cells. A faster CD4-T and B cell recovery in children may be the driver of a better immune competence underpinning superior outcomes in pediatric populations. If age-related thymus involution may reflect suboptimal CD4-T cell differentiation in adults, younger donor selection does not seem to improve recipient post-transplant immune reconstitution.

Cascade Forest-Based Model for Prediction of RNA Velocity.

In recent years, single-cell RNA sequencing technology (scRNA-seq) has developed rapidly and has been widely used in biological and medical research, such as in expression heterogeneity and transcriptome dynamics of single cells. The investigation of RNA velocity is a new topic in the study of cellular dynamics using single-cell RNA sequencing data. It can recover directional dynamic information from single-cell transcriptomics by linking measurements to the underlying dynamics of gene expression. Predicting the RNA velocity vector of each cell based on its gene expression data and formulating RNA velocity prediction as a classification problem is a new research direction. In this paper, we develop a cascade forest model to predict RNA velocity. Compared with other popular ensemble classifiers, such as XGBoost, RandomForest, LightGBM, NGBoost, and TabNet, it performs better in predicting RNA velocity. This paper provides guidance for researchers in selecting and applying appropriate classification tools in their analytical work and suggests some possible directions for future improvement of classification tools.

Optical Tracking of Surfactant-Tuned Bacterial Adhesion: a Single-Cell Imaging Study.

Probing the interfacial dynamics of single bacterial cells in complex environments is crucial for understanding the microbial biofilm formation process and developing antifouling materials, but it remains a challenge. Here, we studied single bacterial interfacial behaviors modulated by surfactants via a plasmonic imaging technique. We quantified the adhesion strength of single bacterial cells by plasmonic measurement of potential energy profiles and dissected the mechanism of surfactant-tuned single bacterial adhesion. The presence of surfactant tuned single bacterial adhesion by increasing the thickness of extracellular polymeric substances (EPS) and reducing the degree of EPS cross-linking. The adhesion kinetics and equilibrium state of bacteria attached to the surface confirmed the decrease in adhesion strength tuned by surfactants. The information obtained is valuable for understanding the interaction mechanism between a single bacterial cell and surface, developing new biofilm control strategies, and designing anticontamination materials. IMPORTANCE Studying the interfacial dynamic of single bacteria in complex environments is crucial for understanding the microbial biofilm formation process and developing antifouling materials. However, quantifying the interactions between microorganisms and surfaces in the presence of pollution at the single-cell level remains a great challenge. This paper presents the analysis of single bacterial interfacial behaviors modulated by surfactants and quantification of the adhesion strength via a plasmonic imaging technique. Our study provided insights into the mechanism of initial bacterial adhesion, facilitating our understanding of the adhesion process at the microscopic scale, and is of great value for controlling membrane fouling biofilm formation.

Quantitative RNA imaging in single live cells reveals age-dependent asymmetric inheritance.

Asymmetric inheritance of cellular content through cell division plays an important role in cell viability and fitness. The dynamics of RNA segregation are so far largely unaddressed. This is partly due to a lack of approaches to follow RNAs over multiple cellular divisions. Here, we establish an approach to quantify RNA dynamics in single cells across several generations in a microfluidics device by tagging RNAs with the diSpinach aptamer. Using S. cerevisiae as a model, we quantitatively characterize intracellular RNA transport from mothers into their buds. Our results suggest that, at cytokinesis, ENO2 diSpinach RNA is preferentially distributed to daughters. This asymmetric RNA segregation depends on the lifespan regulator Sir2 and decreases with increasing replicative age of mothers but does not result from increasing cell size during aging. Overall, our approach opens more opportunities to study RNA dynamics and inheritance in live budding yeast at the single-cell level.

ODP011 Single Nuclei RNA-Sequencing of Frozen Human Adipose Tissue and PET/CT Imaging Coupled With Deep-Learning Reveals Single-Cell Landscape and Transcriptional Dynamics Related to Adipocyte Hypertrophy and in Vivo Dysmetabolism

Abstract Background Adipocyte hypertrophy is associated with glucose and lipid dysmetabolism, ectopic fat accumulation, and metabolic complications independently of the level of obesity [1]. In this study, we aimed to explore potential mechanisms driving the obesity-independent, hypertrophy-associated dysmetabolism at a single-cell resolution and to assess the applicability of single nuclei RNA-sequencing of flash-frozen human subcutaneous adipose tissue (AT) to probe for these metabolic differences. Methods In 40 healthy participants, peri-umbilical subcutaneous adipose tissue biopsies were collected by needle aspiration to determine adipocyte size. Tissue uptake of dietary fat was quantified via oral 18-fluoro-thiaheptadecanoic acid administration with positron emission tomography (PET) [2]. Rates of fatty acid oxidation and esterification were measured using intravenous [11C]-palmitate with PET and multi-compartmental modeling. PET/CT segmentation using deep convolutional neural networks was done with DeepImageTranslator [3,4]. Single nuclei RNA-sequencing was performed in two individuals with AT hypertrophy vs. hyperplasia but matched for the same sex, ethnicity, glucose-tolerance status, BMI, total and percent body fat, and waist circumference. Nuclei were extracted from frozen AT and purified by flow cytometry following a protocol that we have developed and validated. Capture of single nuclei and preparation of RNA libraries were done with 10X Genomics technology. Results Unsupervised clustering identified a number of cell-type markers identical to those previously reported in murine epididymal AT and human AT. We found reduced quantities of fibroadipogenic cells per volume of AT with fat cell hypertrophy. Furthermore, the fraction of DPP4+ fibroblasts is increased with AT hypertrophy. Analyses of RNA velocity shows decreased velocity of adipocyte transformation from a transcriptionally active (TADA) phenotype to mature adipocytes in AT hypertrophy. Furthermore, in AT hypertrophy, adipocytes from the early TADA stage are destined to become insulin-resistant adipocytes, whereas those in AT hyperplasia are developing into an insulin-sensitive phenotype. Metabolic imaging results show that hypertrophied subcutaneous AT is associated with lower subcutaneous and higher visceral AT volume and dietary fatty acid storage capacity. Conclusion We have documented, for the first time, the applicability and consistency of single-nuclei RNA-sequencing of frozen human subcutaneous AT. We uncovered changes in cell clusters and subclusters of adipocytes, fibroblasts, pericytes, endothelial and immune cells as well as differences in transcriptional dynamics and developmental trajectory. [1] Ye RZ, et al. Endocrine reviews. 2022 Feb;43(1): 35-60. [2] Montastier É, Ye RZ, et al. American Journal of Physiology-Endocrinology and Metabolism. 2021 Jun 1;320(6): E1093-106. [3] R. Z. Ye, et al., DeepImageTranslator: A free, user-friendly graphical interface for image translation using deep-learning and its applications in 3D CT image analysis, SLAS Technology, https://doi.org/10.1016/j.slast.2021.10. 014, [4] Ye EZ, Ye EH, Ye RZ. DeepImageTranslator V2: analysis of multimodal medical images using semantic segmentation maps generated through deep learning. bioRxiv. 2021 Jan 1. Presentation: No date and time listed

Relating individual cell division events to single-cell ERK and Akt activity time courses

Biochemical correlates of stochastic single-cell fates have been elusive, even for the well-studied mammalian cell cycle. We monitored single-cell dynamics of the ERK and Akt pathways, critical cell cycle progression hubs and anti-cancer drug targets, and paired them to division events in the same single cells using the non-transformed MCF10A epithelial line. Following growth factor treatment, in cells that divide both ERK and Akt activities are significantly higher within the S-G2 time window (~ 8.5–40 h). Such differences were much smaller in the pre-S-phase, restriction point window which is traditionally associated with ERK and Akt activity dependence, suggesting unappreciated roles for ERK and Akt in S through G2. Simple metrics of central tendency in this time window are associated with subsequent cell division fates. ERK activity was more strongly associated with division fates than Akt activity, suggesting Akt activity dynamics may contribute less to the decision driving cell division in this context. We also find that ERK and Akt activities are less correlated with each other in cells that divide. Network reconstruction experiments demonstrated that this correlation behavior was likely not due to crosstalk, as ERK and Akt do not interact in this context, in contrast to other transformed cell types. Overall, our findings support roles for ERK and Akt activity throughout the cell cycle as opposed to just before the restriction point, and suggest ERK activity dynamics may be more important than Akt activity dynamics for driving cell division in this non-transformed context.

Analysis of the Human Protein Atlas Weakly Supervised Single-Cell Classification competition

While spatial proteomics by fluorescence imaging has quickly become an essential discovery tool for researchers, fast and scalable methods to classify and embed single-cell protein distributions in such images are lacking. Here, we present the design and analysis of the results from the competition Human Protein Atlas – Single-Cell Classification hosted on the Kaggle platform. This represents a crowd-sourced competition to develop machine learning models trained on limited annotations to label single-cell protein patterns in fluorescent images. The particular challenges of this competition include class imbalance, weak labels and multi-label classification, prompting competitors to apply a wide range of approaches in their solutions. The winning models serve as the first subcellular omics tools that can annotate single-cell locations, extract single-cell features and capture cellular dynamics.