Multiplexed chromatin imaging reveals predominantly pairwise long-range coordination between Drosophila Polycomb genes.

Polycomb (Pc) group proteins are transcriptional regulators with key roles in development, cell identity, and differentiation. Pc-bound chromatin regions form repressive domains that interact in 3D to assemble repressive nuclear compartments. Here, we use multiplexed chromatin imaging to investigate whether Pc compartments involve the clustering of multiple Pc domains during Drosophila development. Notably, 3D proximity between Pc targets is rare and involves predominantly pairwise interactions. These 3D proximities are particularly enhanced in segments where Pc genes are co-repressed. In addition, segment-specific expression of Hox Pc targets leads to their spatial segregation from Pc-repressed genes. Finally, non-Hox Pc targets are more proximal in regions where they are co-expressed. These results indicate that long-range Pc interactions are temporally and spatially regulated during differentiation and development but do not induce frequent clustering of multiple distant Pc genes.

Hi-M: A Multiplex Oligopaint FISH Method to Capture Chromatin Conformations In Situ and Accompanying Open-Source Acquisition Software.

The simultaneous observation of three-dimensional (3D) chromatin structure and transcription in single cells is critical to understand how DNA is organized inside cells and how this organization influences or is affected by other processes, such as transcription. We have recently introduced an innovative technology known as Hi-M, which enables the sequential tagging, 3D visualization, and precise localization of multiple genomic DNA regions alongside RNA expression within individual cells. In this chapter, we present a comprehensive guide outlining the creation of probes, as well as sample preparation and labeling. Finally, we provide a step-by-step guide to conduct a complete Hi-M acquisition using our open-source software package, Qudi-HiM, which controls the robotic microscope handling the entire acquisition procedure.

pyHiM: a new open-source, multi-platform software package for spatial genomics based on multiplexed DNA-FISH imaging.

Genome-wide ensemble sequencing methods improved our understanding of chromatin organization in eukaryotes but lack the ability to capture single-cell heterogeneity and spatial organization. To overcome these limitations, new imaging-based methods have emerged, giving rise to the field of spatial genomics. Here, we present pyHiM, a user-friendly python toolbox specifically designed for the analysis of multiplexed DNA-FISH data and the reconstruction of chromatin traces in individual cells. pyHiM employs a modular architecture, allowing independent execution of analysis steps and customization according to sample specificity and computing resources. pyHiM aims to facilitate the democratization and standardization of spatial genomics analysis.

Contributions of 3D chromatin structure to cell-type-specific gene regulation.

Eukaryotic genomes are organized in 3D in a multiscale manner, and different mechanisms acting at each of these scales can contribute to transcriptional regulation. However, the large single-cell variability in 3D chromatin structures represents a challenge to understand how transcription may be differentially regulated between cell types in a robust and efficient manner. Here, we describe the different mechanisms by which 3D chromatin structure was shown to contribute to cell-type-specific transcriptional regulation. Excitingly, several novel methodologies able to measure 3D chromatin conformation and transcription in single cells in their native tissue context, or to detect the dynamics of cis-regulatory interactions, are starting to allow quantitative dissection of chromatin structure noise and relate it to how transcription may be regulated between different cell types and cell states.

Remote Monitoring of Positive Airway Pressure Data: Challenges, Pitfalls, and Strategies to Consider for Optimal Data Science Applications.

Over recent years, positive airway pressure (PAP) remote monitoring has transformed the management of OSA and produced a large amount of data. Accumulated PAP data provide valuable and objective information regarding patient treatment adherence and efficiency. However, the majority of studies that have analyzed longitudinal PAP remote monitoring have summarized data trajectories in static and simplistic metrics for PAP adherence and the residual apnea-hypopnea index by the use of mean or median values. The aims of this article are to suggest directions for improving data cleaning and processing and to address major concerns for the following data science applications: (1) conditions for residual apnea-hypopnea index reliability, (2) lack of standardization of indicators provided by different PAP models, (3) missing values, and (4) consideration of treatment interruptions. To allow fair comparison among studies and to avoid biases in computation, PAP data processing and management should be conducted rigorously with these points in mind. PAP remote monitoring data contain a wealth of information that currently is underused in the field of sleep research. Improving the quality and standardizing data handling could facilitate data sharing among specialists worldwide and enable artificial intelligence strategies to be applied in the field of sleep apnea.

Median eminence blood flow influences food intake by regulating ghrelin access to the metabolic brain.

Central integration of peripheral appetite-regulating signals ensures maintenance of energy homeostasis. Thus, plasticity of circulating molecule access to neuronal circuits involved in feeding behavior plays a key role in the adaptive response to metabolic changes. However, the mechanisms involved remain poorly understood despite their relevance for therapeutic development. Here, we investigated the role of median eminence mural cells, including smooth muscle cells and pericytes, in modulating gut hormone effects on orexigenic/anorexigenic circuits. We found that conditional activation of median eminence vascular cells impinged on local blood flow velocity and altered ghrelin-stimulated food intake by delaying ghrelin access to target neurons. Thus, activation of median eminence vascular cells modulates food intake in response to peripheral ghrelin by reducing local blood flow velocity and access to the metabolic brain.

Metabolic Stress Impairs Pericyte Response to Optogenetic Stimulation in Pancreatic Islets.

Pancreatic islets are highly vascularized micro-organs ensuring whole body glucose homeostasis. Islet vascular cells play an integral part in sustaining adequate insulin release by beta cells. In particular, recent studies have demonstrated that islet pericytes regulate local blood flow velocity and are required for maintenance of beta cell maturity and function. In addition, increased metabolic demand accompanying obesity alters islet pericyte morphology. Here, we sought to explore the effects of metabolic stress on islet pericyte functional response to stimulation in a mouse model of type 2 diabetes, directly in the pancreas in vivo . We found that high fat diet induced islet pericyte hypertrophy without alterations in basal local blood flow. However, optogenetic stimulation of pericyte activity revealed impaired islet vascular responses, despite increased expression of genes encoding proteins directly or indirectly involved in cell contraction. These findings suggest that metabolic stress impinges upon islet pericyte function, which may contribute to beta cell failure during T2D.

Bayesian Structural Time Series With Synthetic Controls for Evaluating the Impact of Mask Changes in Residual Apnea-Hypopnea Index Telemonitoring Data.

OBJECTIVE: In obstructive sleep apnea patients on continuous positive airway pressure (CPAP) treatment there is growing evidence for a significant impact of the type of mask on the residual apnea-hypopnea index (rAHI). Here, we propose a method for automatically classifying the impact of mask changes on rAHI. METHODS: From a CPAP telemonitoring database of 3,581 patients, an interrupted time series design was applied to rAHI time series at a patient level to compare the observed rAHI after a mask-change with what would have occurred without the mask-change. rAHI time series before mask changes were modelled using different approaches. Mask changes were classified as: no effect, harmful, beneficial. The best model was chosen based on goodness-of-fit metrics and comparison with blinded classification by an experienced respiratory physician. RESULTS: Bayesian structural time series with synthetic controls was the best approach in terms of agreement with the physician.s classification, with an accuracy of 0.79. Changes from nasal to facial mask were more often harmful than beneficial: 13.4% vs 7.6% (p-value < 0.05), with a clinically relevant increase in average rAHI greater than 8 events/hour in 4.6% of cases. Changes from facial to nasal mask were less often harmful: 6.0% vs 11.4% (p-value < 0.05). CONCLUSION: We propose an end-to-end method to automatically classify the impact of mask changes over fourteen days after a switchover. SIGNIFICANCE: The proposed automated analysis of the impact of changes in health device settings or accessories presents a novel tool to include in remote monitoring platforms for raising alerts after harmful interventions.

Hidden Markov model segmentation to demarcate trajectories of residual apnoea-hypopnoea index in CPAP-treated sleep apnoea patients to personalize follow-up and prevent treatment failure.

BACKGROUND: Continuous positive airway pressure (CPAP), the reference treatment for obstructive sleep apnoea (OSA), is used by millions of individuals worldwide with remote telemonitoring providing daily information on CPAP usage and efficacy, a currently underused resource. Here, we aimed to implement data science methods to provide tools for personalizing follow-up and preventing treatment failure. METHODS: We analysed telemonitoring data from adults prescribed CPAP treatment. Our primary objective was to use Hidden Markov models (HMMs) to identify the underlying state of treatment efficacy and enable early detection of deterioration. Secondary goals were to identify clusters of rAHI trajectories which need distinct therapeutic strategies. RESULTS: From telemonitoring records of 2860 CPAP-treated patients (age: 66.31 ± 12.92 years, 69.9% male), HMM estimated three states differing in variability within a given state and probability of shifting from one state to another. The daily inferred state informs on the need for a personalized action, while the sequence of states is a predictive indicator of treatment failure. Six clusters of rAHI trajectories were identified ranging from well-controlled patients (cluster 0: 669 (23%); mean rAHI 0.58 ± 0.59 events/h) to the most unstable (cluster 5: 470 (16%); mean rAHI 9.62 ± 5.62 events/h). CPAP adherence was 30 min higher in cluster 0 compared to clusters 4 and 5 (P value < 0.01). CONCLUSION: This new approach based on HMM might constitute the backbone for deployment of patient-centred CPAP management improving the personalized interpretation of telemonitoring data, identifying individuals for targeted therapy and preventing treatment failure or abandonment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13167-021-00264-z.

Apnea-hypopnea index supplied by CPAP devices: time for standardization?

BACKGROUND/OBJECTIVE: For obstructive sleep apnea (OSA) patients on continuous positive airway pressure (CPAP) treatment, the apnea-hypopnea index (AHI) is a key measure of treatment efficacy. However, the residual AHI is CPAP brand specific. Here, we studied changes in residual AHI in patients who used two different brands over their treatment history. PATIENTS/METHODS: Using our CPAP telemonitoring database of 3102 patients, we compared the residual AHI of 69 patients before and after change in their CPAP device. RESULTS: A paired Wilcoxon signed-rank test revealed a significant difference between brands in the reported AHI, which might be clinically misleading. CONCLUSIONS: These findings suggest that physicians should be alerted to the differences between brands and learned societies should push for standardization of AHI reporting.

Maternal hypothyroidism in mice influences glucose metabolism in adult offspring.

AIMS/HYPOTHESIS: During pregnancy, maternal metabolic disease and hormonal imbalance may alter fetal beta cell development and/or proliferation, thus leading to an increased risk for developing type 2 diabetes in adulthood. Although thyroid hormones play an important role in fetal endocrine pancreas development, the impact of maternal hypothyroidism on glucose homeostasis in adult offspring remains poorly understood. METHODS: We investigated this using a mouse model of hypothyroidism, induced by administration of an iodine-deficient diet supplemented with propylthiouracil during gestation. RESULTS: Here, we show that, when fed normal chow, adult mice born to hypothyroid mothers were more glucose-tolerant due to beta cell hyperproliferation (two- to threefold increase in Ki67-positive beta cells) and increased insulin sensitivity. However, following 8 weeks of high-fat feeding, these offspring gained 20% more body weight, became profoundly hyperinsulinaemic (with a 50% increase in fasting insulin concentration), insulin-resistant and glucose-intolerant compared with controls from euthyroid mothers. Furthermore, altered glucose metabolism was maintained in a second generation of animals. CONCLUSIONS/INTERPRETATION: Therefore, gestational hypothyroidism induces long-term alterations in endocrine pancreas function, which may have implications for type 2 diabetes prevention in affected individuals.

An exoskeleton controlled by an epidural wireless brain-machine interface in a tetraplegic patient: a proof-of-concept demonstration.

BACKGROUND: Approximately 20% of traumatic cervical spinal cord injuries result in tetraplegia. Neuroprosthetics are being developed to manage this condition and thus improve the lives of patients. We aimed to test the feasibility of a semi-invasive technique that uses brain signals to drive an exoskeleton. METHODS: We recruited two participants at Clinatec research centre, associated with Grenoble University Hospital, Grenoble, France, into our ongoing clinical trial. Inclusion criteria were age 18-45 years, stability of neurological deficits, a need for additional mobility expressed by the patient, ambulatory or hospitalised monitoring, registration in the French social security system, and signed informed consent. The exclusion criteria were previous brain surgery, anticoagulant treatments, neuropsychological sequelae, depression, substance dependence or misuse, and contraindications to magnetoencephalography (MEG), EEG, or MRI. One participant was excluded because of a technical problem with the implants. The remaining participant was a 28-year-old man, who had tetraplegia following a C4-C5 spinal cord injury. Two bilateral wireless epidural recorders, each with 64 electrodes, were implanted over the upper limb sensorimotor areas of the brain. Epidural electrocorticographic (ECoG) signals were processed online by an adaptive decoding algorithm to send commands to effectors (virtual avatar or exoskeleton). Throughout the 24 months of the study, the patient did various mental tasks to progressively increase the number of degrees of freedom. FINDINGS: Between June 12, 2017, and July 21, 2019, the patient cortically controlled a programme that simulated walking and made bimanual, multi-joint, upper-limb movements with eight degrees of freedom during various reach-and-touch tasks and wrist rotations, using a virtual avatar at home (64·0% [SD 5·1] success) or an exoskeleton in the laboratory (70·9% [11·6] success). Compared with microelectrodes, epidural ECoG is semi-invasive and has similar efficiency. The decoding models were reusable for up to approximately 7 weeks without recalibration. INTERPRETATION: These results showed long-term (24-month) activation of a four-limb neuroprosthetic exoskeleton by a complete brain-machine interface system using continuous, online epidural ECoG to decode brain activity in a tetraplegic patient. Up to eight degrees of freedom could be simultaneously controlled using a unique model, which was reusable without recalibration for up to about 7 weeks. FUNDING: French Atomic Energy Commission, French Ministry of Health, Edmond J Safra Philanthropic Foundation, Fondation Motrice, Fondation Nanosciences, Institut Carnot, Fonds de Dotation Clinatec.

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review.

Brain-Computer Interfaces (BCIs) are systems that establish a direct communication pathway between the users' brain activity and external effectors. They offer the potential to improve the quality of life of motor-impaired patients. Motor BCIs aim to permit severely motor-impaired users to regain limb mobility by controlling orthoses or prostheses. In particular, motor BCI systems benefit patients if the decoded actions reflect the users' intentions with an accuracy that enables them to efficiently interact with their environment. One of the main challenges of BCI systems is to adapt the BCI's signal translation blocks to the user to reach a high decoding accuracy. This paper will review the literature of data-driven and user-specific transducer design and identification approaches and it focuses on internally-paced motor BCIs. In particular, continuous kinematic biomimetic and mental-task decoders are reviewed. Furthermore, static and dynamic decoding approaches, linear and non-linear decoding, offline and real-time identification algorithms are considered. The current progress and challenges related to the design of clinical-compatible motor BCI transducers are additionally discussed.

Integrin ß1 Optimizes Diabetogenic T Cell Migration and Function in the Pancreas.

T cell search behavior is dictated by their need to encounter their specific antigen to eliminate target cells. However, mechanisms controlling effector T cell motility are highly tissue-dependent. Specifically, how diabetogenic T cells encounter their target beta cells in dispersed islets throughout the pancreas (PA) during autoimmune diabetes remains unclear. Using intra-vital 2-photon microscopy in a mouse model of diabetes, we found that CXCR3 chemokine downregulated CD8+ T cell motility specifically within islets, promoting effector cell confinement to their target sites. By contrast, T cell velocity and directionality in the exocrine tissue were enhanced along blood vessels and extracellular matrix fibers. This guided migration implicated integrin-dependent interactions, since integrin blockade impaired exocrine T cell motility. In addition, integrin ß1 blockade decreased CD4+ T cell effector phenotype specifically in the PA. Thus, we unveil an important role for integrins in the PA during autoimmune diabetes that may have important implications for the design of new therapies.

Imaging endocrinology in animal models of endocrine disease.

Endocrine organs secrete a variety of hormones involved in the regulation of a multitude of body functions. Although pancreatic islets were discovered at the turn of the 19th century, other endocrine glands remained commonly described as diffuse endocrine systems. Over the last two decades, development of new imaging techniques and genetically-modified animals with cell-specific fluorescent tags or specific hormone deficiencies have enabled in vivo imaging of endocrine organs and revealed intricate endocrine cell network structures and plasticity. Overall, these new tools have revolutionized our understanding of endocrine function. The overarching aim of this Review is to describe the current mechanistic understanding that has emerged from imaging studies of endocrine cell network structure/function relationships in animal models, with a particular emphasis on the pituitary gland and the endocrine pancreas.

Adult muscle-derived stem cells engraft and differentiate into insulin-expressing cells in pancreatic islets of diabetic mice.

BACKGROUND: Pancreatic beta cells are unique effectors in the control of glucose homeostasis and their deficiency results in impaired insulin production leading to severe diabetic diseases. Here, we investigated the potential of a population of nonadherent muscle-derived stem cells (MDSC) from adult mouse muscle to differentiate in vitro into beta cells when transplanted as undifferentiated stem cells in vivo to compensate for beta-cell deficiency. RESULTS: In vitro, cultured MDSC spontaneously differentiated into insulin-expressing islet-like cell clusters as revealed using MDSC from transgenic mice expressing GFP or mCherry under the control of an insulin promoter. Differentiated clusters of beta-like cells co-expressed insulin with the transcription factors Pdx1, Nkx2.2, Nkx6.1, and MafA, and secreted significant levels of insulin in response to glucose challenges. In vivo, undifferentiated MDSC injected into streptozotocin (STZ)-treated mice engrafted within 48 h specifically to damaged pancreatic islets and were shown to differentiate and express insulin 10-12 days after injection. In addition, injection of MDSC into hyperglycemic diabetic mice reduced their blood glucose levels for 2-4 weeks. CONCLUSION: These data show that MDSC are capable of differentiating into mature pancreatic beta islet-like cells, not only upon culture in vitro, but also in vivo after systemic injection in STZ-induced diabetic mouse models. Being nonteratogenic, MDSC can be used directly by systemic injection, and this potential reveals a promising alternative avenue in stem cell-based treatment of beta-cell deficiencies.

CD4+ T Helper Cells Play a Key Role in Maintaining Diabetogenic CD8+ T Cell Function in the Pancreas.

Autoreactive CD8+ and CD4+ T cells have been assigned independent key roles in the destruction of insulin-producing beta cells resulting in type 1 diabetes. Although CD4 help for the generation of efficient CD8+ T cell responses in lymphoid tissue has been extensively described, whether these two cell populations cooperate in islet destruction in situ remains unclear. By using intravital 2-photon microscopy in a mouse model of diabetes, we visualized both effector T cell populations in the pancreas during disease onset. CD4+ T helper cells displayed a much higher arrest in the exocrine tissue than islet-specific CD8+ T cells. This increased arrest was major histocompatibility complex (MHC) class II-dependent and locally correlated with antigen-presenting cell recruitment. CD8+ T cells deprived of continued CD4 help specifically in the pancreas, through blocking MHC class II recognition, failed to maintain optimal effector functions, which contributed to hamper diabetes progression. Thus, we provide novel insight in the cellular mechanisms regulating effector T cell functionality in peripheral tissues with important implications for immunotherapies.

Metabolism Regulates Exposure of Pancreatic Islets to Circulating Molecules In Vivo.

Pancreatic ß-cells modulate insulin secretion through rapid sensing of blood glucose and integration of gut-derived signals. Increased insulin demand during pregnancy and obesity alters islet function and mass and leads to gestational diabetes mellitus and type 2 diabetes in predisposed individuals. However, it is unclear how blood-borne factors dynamically access the islets of Langerhans. Thus, understanding the changes in circulating molecule distribution that accompany compensatory ß-cell expansion may be key to developing novel antidiabetic therapies. Here, using two-photon microscopy in vivo in mice, we demonstrate that islets are almost instantly exposed to peaks of circulating molecules, which rapidly pervade the tissue before clearance. In addition, both gestation and short-term high-fat-diet feeding decrease molecule extravasation and uptake rates in vivo in islets, independently of ß-cell expansion or islet blood flow velocity. Together, these data support a role for islet vascular permeability in shaping ß-cell adaptive responses to metabolic demand by modulating the access and sensing of circulating molecules.

Switching Markov decoders for asynchronous trajectory reconstruction from ECoG signals in monkeys for BCI applications.

Brain-Computer Interfaces (BCIs) are systems which translate brain neural activity into commands for external devices. BCI users generally alternate between No-Control (NC) and Intentional Control (IC) periods. NC/IC discrimination is crucial for clinical BCIs, particularly when they provide neural control over complex effectors such as exoskeletons. Numerous BCI decoders focus on the estimation of continuously-valued limb trajectories from neural signals. The integration of NC support into continuous decoders is investigated in the present article. Most discrete/continuous BCI hybrid decoders rely on static state models which don't exploit the dynamic of NC/IC state succession. A hybrid decoder, referred to as Markov Switching Linear Model (MSLM), is proposed in the present article. The MSLM assumes that the NC/IC state sequence is generated by a first-order Markov chain, and performs dynamic NC/IC state detection. Linear continuous movement models are probabilistically combined using the NC and IC state posterior probabilities yielded by the state decoder. The proposed decoder is evaluated for the task of asynchronous wrist position decoding from high dimensional space-time-frequency ElectroCorticoGraphic (ECoG) features in monkeys. The MSLM is compared with another dynamic hybrid decoder proposed in the literature, namely a Switching Kalman Filter (SKF). A comparison is additionally drawn with a Wiener filter decoder which infers NC states by thresholding trajectory estimates. The MSLM decoder is found to outperform both the SKF and the thresholded Wiener filter decoder in terms of False Positive Ratio and NC/IC state detection error. It additionally surpasses the SKF with respect to the Pearson Correlation Coefficient and Root Mean Squared Error between true and estimated continuous trajectories.