Biology subjects

Peng, T.

Publications and source records attributed to Peng, T..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Identification of Meibomian gland stem cell populations and mechanisms of aging

Meibomian glands secrete lipid-rich meibum, which prevents tear evaporation. Aging-related Meibomian gland shrinkage may result in part from stem cell exhaustion and is associated with evaporative dry eye disease, a common condition lacking effective treatment. The identities and niche of Meibomian gland stem cells and the signals controlling their activity are poorly defined. Using snRNA-seq, in vivo lineage tracing, ex vivo live imaging, and genetic studies in mice, we identified markers for stem cell populations that maintain distinct regions of the gland and uncovered Hh signaling as a key regulator of stem cell proliferation. Consistent with this, human Meibomian gland carcinoma exhibited increased Hh signaling. Aged glands displayed decreased Hh and EGF signaling, deficient innervation, and loss of collagen I in niche fibroblasts, indicating that alterations in both glandular epithelial cells and their surrounding microenvironment contribute to age-related degeneration. These findings suggest new approaches to treat aging-associated Meibomian gland loss.

cell biology↗

bioRxiv2024

Senescent fibroblasts in the tumor stroma rewire lung cancer metabolism and plasticity

Senescence has been demonstrated to either inhibit or promote tumorigenesis. Resolving this paradox requires spatial mapping and functional characterization of senescent cells in the native tumor niche. Here, we identified senescent p16Ink4a+ cancer-associated fibroblasts with a secretory phenotype that promotes fatty acid uptake and utilization by aggressive lung adenocarcinoma driven by Kras and p53 mutations. Furthermore, rewiring of lung cancer metabolism by p16Ink4a+ cancer- associated fibroblasts also altered tumor cell identity to a highly plastic/dedifferentiated state associated with progression in murine and human LUAD. Our ex vivo senolytic screening platform identified XL888, a HSP90 inhibitor, that cleared p16Ink4a+ cancer- associated fibroblasts in vivo. XL888 administration after establishment of advanced lung adenocarcinoma significantly reduced tumor burden concurrent with the loss of plastic tumor cells. Our study identified a druggable component of the tumor stroma that fulfills the metabolic requirement of tumor cells to acquire a more aggressive phenotype.

cancer biology↗

bioRxiv2024

Hallmarks of tumor-experienced T cells are absent in multiple myeloma patients from diagnosis through maintenance therapy

Dysregulation of the bone marrow (BM) niche in multiple myeloma (MM) alters the composition and state of resident immune cells, potentially impeding anti-tumor immunity. One common mechanism of immune inhibition in solid tumors is the induction of exhaustion in tumor-specific T cells. However, the extent of T cell tumor recognition and exhaustion is not well-characterized in MM. As the specific mechanisms of immune evasion are critical for devising effective therapeutic strategies, we deeply profiled the CD8+ T cell compartment of newly-diagnosed MM (NDMM) patients for evidence of tumor reactivity and T cell exhaustion. We applied single-cell multi-omic sequencing and antigen-specific mass cytometry to longitudinal BM and peripheral blood (PB) samples taken from timepoints spanning from diagnosis through induction therapy, autologous stem cell transplant (ASCT), and maintenance therapy. We identified an exhausted-like population that lacked several canonical exhaustion markers, was not significantly enriched in NDMM patients, and consisted of small, nonpersistent clones. We also observed an activated population with increased frequency in the PB of NDMM patients exhibiting phenotypic and clonal features consistent with homeostatic, antigen-nonspecific activation. However, there was no evidence of "tumor-experienced" T cells displaying hallmarks of terminal exhaustion and/or tumor-specific activation/expansion in NDMM patients at any timepoint.

immunology↗

bioRxiv2024

Mean Shift Clustering as a Loss Function for Accurate and Segmentation-aware Localization of Macromolecules in Cryo-electron Tomography

Cryo-electron tomography allows us to visualize and analyze the native cellular environment on a molecular level in 3D. To reliably study structures and interactions of proteins, they need to be accurately localized. Recent detection methods train a segmentation network and use post-processing to determine protein locations, often leading to inaccurate and inconsistent locations. We present an end-to-end learning approach for more accurate protein center identification by introducing a differentiable, scoremap-guided Mean Shift clustering implementation. To make training computationally feasible, we sample random cluster points instead of processing the entire image. We show that our Mean Shift loss leads to more accurate cluster center positions compared to the classical Dice loss. When combining these loss functions, we can enhance 3D protein shape preservation and improve clustering with more accurate, localization-focused score maps. In addition to improved protein localization, our method provides more efficient training with sparse ground truth annotations, due to our point sampling strategy.

bioinformatics↗

bioRxiv2024

MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography

Cryo-electron tomography (cryo-ET) provides unique insights into macromolecular complexes in their native environments, yet membrane analysis remains a major bottleneck due to low signal-to-noise ratios, missing wedge artifacts, and the complexity of membrane-associated proteins. Existing tools often require extensive manual annotation, struggle with generalization across datasets, and lack integrated solutions for segmentation, protein localization, and quantitative analysis. We introduce MemBrain v2, a deep learning-enabled framework that unifies these tasks into a streamlined pipeline. MemBrain-seg leverages a diverse, collaboratively generated training dataset and specialized model training strategies to achieve generalizable membrane segmentation across variable tomographic conditions. MemBrain-pick enables data-efficient localization of membrane-bound proteins by integrating geometric constraints with deep learning, reducing the need for extensive manual annotation. MemBrain-stats provides quantitative insights into protein distributions, computing spatial metrics to analyze intra-membrane particle organization. MemBrain v2 integrates seamlessly into cryo-ET workflows, providing an accessible and structured approach to membrane analysis. The full package is available at https://github.com/CellArchLab/MemBrain-v2.

bioinformatics↗

bioRxiv2023

ATF6 activation alters colonic lipid metabolism causing tumor-associated microbial adaptation

Endoplasmic reticulum unfolded protein responses (UPRER) contribute to cancer development and the activating transcription factor 6 (ATF6) is involved in microbiota-dependent tumorigenesis. Here, we substantiate the clinical relevance of ATF6 in early-onset and late colorectal cancer patient cohorts. Transcriptional analysis in intestinal epithelial cells (IEC) of ATF6 transgenic mice (nATF6IEC) identified bacteria-specific changes in cellular metabolism enriched for fatty acid biosynthesis. Untargeted metabolomics and isotype-labeling confirmed ATF6-related enrichment of long chain fatty acids in colonic tissue of patients, mice and organoid cultures. FASN inhibition and microbiota transfer in germ-free nATF6IEC mice confirmed the causal involvement of ATF6-induced lipid alterations in tumorigenesis. The selective expansion of tumor-relevant microbial taxa was mechanistically linked to long chain fatty acid exposure, using bioorthogonal non-canonical amino acid tagging (BONCAT) and growth analysis of Desulfovibrio isolates. We postulate chronic ATF6 signaling in the epithelium to select for tumor-promoting microbiota by altering lipid metabolism. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/565267v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@be3756org.highwire.dtl.DTLVardef@231da4org.highwire.dtl.DTLVardef@16f3aaeorg.highwire.dtl.DTLVardef@1758fe3_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract:C_FLOATNO Chronic ATF6 signaling in the colonic epithelium alters lipid metabolism to select a tumor-promoting microbiota C_FIG O_LIBiallelic expression of activated ATF6 (p50 nuclear fragment) in intestinal epithelial cells (nATF6IEC) induces spontaneous colon tumors in SPF but not GF mice C_LIO_LIMechanistically, biallelic SPF nATF6IEC mice alter colonic lipid metabolism, including the upregulation of LCFAs and Fasn C_LIO_LIInhibition of FASN prevents colon tumor formation in mice, and reduces the tumor-promoting potential of the intestinal microbiota (FMT) C_LIO_LIExposure of fl/fl control mouse microbiota to LCFAs ex vivo translationally activates tumor-associated bacteria, including Desulfovibrio fairfieldensis C_LIO_LIHuman CRC patients show ATF6 upregulation, FASN co-occurrence and increased LCFAs in T tissue C_LIO_LIATF6 activity links with CRC-associated microbiota in patients, including Desulfovibrio C_LI Created with BioRender.com nATF6: activated activating transcription factor 6; LCFA: long-chain fatty acids; SAFA: saturated fatty acids; Fasn: fatty acid synthase; C75 i.p.: intraperitoneal injection of the Fasn inhibitor C75.

cancer biology↗

bioRxiv2023

Development of properly-polarized trophoblast stem cell-derived organoids to model early human pregnancy

The development of human trophoblast stem cells (hTSC) and stem cell-derived trophoblast organoids has enabled investigation of placental physiology and disease and early maternal-fetal interactions during a stage of human pregnancy that previously had been severely restricted. A key shortcoming in existing trophoblast organoid methodologies is the non-physiologic position of the syncytiotrophoblast (STB) within the inner portion of the organoid, which neither recapitulates placental villous morphology in vivo nor allows for facile modeling of STB exposure to the endometrium or the contents of the intervillous space. Here we have successfully established properly-polarized human trophoblast stem cell (hTSC)-sourced organoids with STB forming on the surface of the organoid. These organoids can also be induced to give rise to the extravillous trophoblast (EVT) lineage with HLA-G+ migratory cells that invade into an extracellular matrix-based hydrogel. Compared to previous hTSC organoid methods, organoids created by this method more closely mimic the architecture of the developing human placenta and provide a novel platform to study normal and abnormal human placental development and to model exposures to pharmaceuticals, pathogens and environmental insults. MotivationHuman placental organoids have been generated to mimic physiological cell-cell interactions. However, those published models derived from human trophoblast stem cells (hTSCs) or placental villi display a non-physiologic "inside-out" morphology. In vivo, the placental villi have an outer layer of syncytialized cells that are in direct contact with maternal blood, acting as a conduit for gas and nutrient exchange, and an inner layer of progenitor, single cytotrophoblast cells that fuse to create the syncytiotrophoblast layer. Existing "inside-out" models put the cytotrophoblast cells in contact with culture media and substrate, making physiologic interactions between syncytiotrophoblast and other cells/tissues and normal and pathogenic exposures coming from maternal blood difficult to model. The goal of this study was to develop an hTSC-derived 3-D human trophoblast organoid model that positions the syncytiotrophoblast layer on the outside of the multicellular organoid. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=69 SRC="FIGDIR/small/560327v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@7f1e7forg.highwire.dtl.DTLVardef@1ab41d9org.highwire.dtl.DTLVardef@7046d5org.highwire.dtl.DTLVardef@13a7542_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2023

Group 2 innate lymphoid cells constrain type 3/17 lymphocytes in shared stromal niches to restrict liver fibrosis

Group 2 innate lymphoid cells (ILC2s) cooperate with adaptive Th2 cells as key organizers of tissue type 2 immune responses, while a spectrum of innate and adaptive lymphocytes coordinate early type 3/17 immunity. Both type 2 and type 3/17 lymphocyte associated cytokines are linked to tissue fibrosis, but how their dynamic and spatial topographies may direct beneficial or pathologic organ remodelling is unclear. Here we used volumetric imaging in models of liver fibrosis, finding accumulation of periportal and fibrotic tract IL-5+ lymphocytes, predominantly ILC2s, in close proximity to expanded type 3/17 lymphocytes and IL-33high niche fibroblasts. Ablation of IL-5+ lymphocytes worsened carbon tetrachloride-and bile duct ligation-induced liver fibrosis with increased niche IL-17A+ type 3/17 lymphocytes, predominantly {gamma}{delta} T cells. In contrast, concurrent ablation of IL-5+ and IL-17A+ lymphocytes reduced this progressive liver fibrosis, suggesting a cross-regulation of type 2 and type 3 lymphocytes at specialized fibroblast niches that tunes hepatic fibrosis.

immunology↗

bioRxiv2023

CellDeathPred: A Deep Learning framework for Ferroptosis and Apoptosis prediction based on cell painting

Cell death, such as apoptosis and ferroptosis, play essential roles in the process of development, homeostasis, and pathogenesis of acute and chronic diseases. The increasing number of studies investigating cell death types in various diseases, particularly cancer and degenerative diseases, has raised hopes for their modulation in disease therapies. However, identifying the presence of a particular cell death type is not an obvious task, as it requires computationally intensive work and costly experimental assays. To address this challenge, we present CellDeathPred, a novel deep learning framework that uses high-content-imaging based on cell painting to distinguish cells undergoing ferroptosis or apoptosis from healthy cells. In particular, we incorporate a deep neural network that effectively embeds microscopic images into a representative and discriminative latent space, classifies the learned embedding into cell death modalities and optimizes the whole learning using the supervised contrastive loss function. We assessed the efficacy of the proposed framework using cell painting microscopy datasets from human HT-1080 cells, where multiple inducers of ferroptosis and apoptosis were used to trigger cell death. Our model confidently separates ferroptotic and apoptotic cells from healthy controls, with an averaged accuracy of 95% on non-confocal datasets, supporting the capacity of the CellDeathPred framework for cell death discovery.

cell biology↗

bioRxiv2023

A multidimensional atlas of human glioblastoma organoids reveals highly coordinated molecular networks and effective drugs

Recent advances in the genomics of glioblastoma (GBM) led to the introduction of molecular neuropathology but failed to translate into treatment improvement. This is largely attributed to the genetic and phenotypic heterogeneity of GBM, which are considered the major obstacle to GBM therapy. Here, we use advanced human GBM organoid (LEGO: Laboratory Engineered Glioblastoma Organoid) and provide an unprecedented comprehensive characterization of LEGO models using single-cell transcriptome, DNA methylome, metabolome, lipidome, proteome, and phospho-proteome analysis. We discovered that genetic heterogeneity dictates functional heterogeneity across molecular layers and demonstrates that NF1 mutation drives mesenchymal signature. Most importantly, we found that glycerol lipid reprogramming is a hallmark of GBM, and several targets and drugs were discovered along this line. We also provide a genotype-based drug reference map using LEGO-based drug screen. This study provides novel human GBM models and a research path toward effective GBM therapy.

cancer biology↗

bioRxiv2022

Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design

Antibodies are versatile proteins that can bind to pathogens and provide effective protection for human body. Recently, deep learning-based computational antibody design has attracted popular attention since it automatically mines the antibody patterns from data that could be complementary to human experiences. However, the computational methods heavily rely on the high-quality antibody structure data, which is quite limited. Besides, the complementarity-determining region (CDR), which is the key component of an antibody that determines the specificity and binding affinity, is highly variable and hard to predict. Therefore, data limitation issue further raises the difficulty of CDR generation for antibodies. Fortunately, there exists a large amount of sequence data of antibodies that can help model the CDR and alleviate the reliance on structured data. By witnessing the success of pre-training models for protein modeling, in this paper, we develop an antibody pre-trained language model and incorporate it into the (antigen-specific) antibody design model in a systemic way. Specifically, we first pre-train an antibody language model based on the sequence data, then propose a one-shot way for sequence and structure generation of CDR to avoid the heavy cost and error propagation from an autoregressive manner, and finally leverage the pre-trained antibody model for the antigen-specific antibody generation model with some carefully designed modules. Through various experiments, we show that our method achieves superior performance over previous baselines on different tasks, such as sequence and structure generation, antigen-binding CDR-H3 design.

molecular biology↗

bioRxiv2022

Junctophilin-2 promotes cardiomyocyte survival by blocking MURF1-mediated Junctin ubiquitination and proteasome-dependent degradation

AimsJunctophilin-2 is required for the development, maturation and integrity of the t-tubule system and the gating stability of RyR2 in cardiomyocytes. This study investigated whether and how junctophilin-2 maintained junctin, a scaffold protein stabilizing RyR2, to prevent cardiomyocyte death under stress. MethodsCardiomyocytes were exposed to conditions of stress including palmitate, doxorubicin, or hypoxia/re-oxygenation. Adenoviral vectors were employed to manipulate expression of junctophilin-2 and junctin in cardiomyocytes. Molecular/cellular/biochemical analyses were conducted. ResultsDifferent conditions of stress decreased junctophilin-2 expression through aberrant autophagy and concomitantly induced a reduction of junctin protein in cardiomyocytes. Over-expression of junctophilin-2 preserved the protein levels of junctin and attenuated cytosolic Ca2+ and apoptosis in cardiomyocytes under stress. Knockdown of junctophilin-2 reproduced the detrimental phenotypes of stress in cardiomyocytes. Notably, over-expression of junctin prevented cardiomyocyte death under stress whereas knockdown of junctin offset the protective effects conferred by junctophilin-2 over-expression. Mechanistically, junctophilin-2 blocked MURF1-junctin interaction thereby preventing junctin ubiquitination and proteasome-dependent degradation. Mass spectrometry analysis identified multiple ubiquitination sites on the junctin protein and the non-ubiquitinated junctin mutant (K8A/K102A/K107A/K140A) was resistant to degradation. ConclusionsThis study uncovers an unrecognized role of junctophilin-2 in preventing junctin ubiquitination and degradation in maintaining cytosolic Ca2+ homeostasis. Both junctophilin-2 and junctin represent two new survival factors of cardiomyocytes and thus, may be new therapeutic targets for cardiac protection.

molecular biology↗

bioRxiv2022

Early Life Exposure to Queen Mandibular Pheromone Mediates Persistent Transcriptional Changes in the Brain of Honey bee Foragers

How behavior in insect societies is regulated remains a fundamental question in sociobiology. In hymenopteran societies, the queen plays a crucial role in regulating group behavior by affecting individual behavior, physiology, and lifespan through worker gene expression. Honey bee (Apis mellifera) queens signal their presence via the queen mandibular pheromone (QMP). While QMP has been shown to influence the behavior of young workers, we know little about its long-term molecular impacts on workers and whether these pheromone effects depend on an early sensitive period in the life of a worker. Here we demonstrate that QMP treatment strongly impacts long-term forager gene expression in the mushroom bodies, antennal lobes, and antennae, but only if bees were treated early in life (1-2 days of age). Contrary to our expectation, foraging activity was not impacted by QMP treatment in the long-term, but genes important for division of labor, learning, chemosensory perception and aging were differentially expressed in the antennae and brain tissues, suggesting that QMP influences diverse physiological and behavioral processes in workers. Overall, our study suggests a sensitive period early in the life of workers, where the queens presence or absence has strong and potentially livelong effects on transcriptional activity in the central and peripheral nervous system. Significance statementDespite our increasing understanding of how social cues affect gene expression and behavior in social animals, we still know little about the importance of sensitive periods in cue perception for the long-term regulation of gene expression. Honey bees live in complex societies and queen pheromones play a central role in the regulation of worker behavior and division of labor. We tested the exposure to queen pheromone presence and found that there is a sensitive period in the early adult life of workers. Understanding and identifying sensitive periods and their effect on long-term gene transcription in workers in response to changes in the environment will lay an important foundation for a better understanding of how queens shape colony life.

animal behavior and cognition↗

bioRxiv2022

Comprehensive characterization of the transcriptional response to COVID-19 in multiple organs reveals shared signatures across tissues

Infection by Coronavirus SARS-CoV2 is a severe and often deadly disease that has implications for the respiratory system and multiple organs across the human body. While the effects in the lung have been extensively studied, less is known about COVID-19s cellular impact across other organs. Here we contribute a single-nuclei RNA sequencing atlas comprising six human organs across 20 autopsies where we analyzed the transcriptional changes due to COVID-19 in multiple cell types. Computational cross-organ analysis for endothelial cells and macrophages identified systemic transcriptional changes in these cell types in COVID-19 samples. In addition, analysis of signaling pathways from multiple datasets showed several systemic dysregulations of signaling interaction in different cell types. Altogether, the COVID Tissue Atlas enables the investigation of both cell type-specific and cross-organ transcriptional responses to COVID-19, providing insights into the molecular networks affected by the disease and highlighting novel potential targets for therapies and drug development.

genomics↗

bioRxiv2022

BEN: a generalizable Brain Extraction Net for multimodal MRI data from rodents, nonhuman primates, and humans

Accurate brain tissue extraction on magnetic resonance imaging (MRI) data is crucial for analyzing brain structure and function. While several conventional tools have been optimized to handle human brain data, there have been no generalizable methods to extract brain tissues for multimodal MRI data from rodents, nonhuman primates, and humans. Therefore, developing a flexible and generalizable method for extracting whole brain tissue across species would allow researchers to analyze and compare experiment results more efficiently. Here, we propose a domain-adaptive and semi-supervised deep neural network, named the Brain Extraction Net (BEN), to extract brain tissues across species, MRI modalities, and MR scanners. We have evaluated BEN on 18 independent datasets, including 783 rodent MRI scans, 246 nonhuman primate MRI scans, and 4,601 human MRI scans, covering five species, four modalities, and six MR scanners with various magnetic field strengths. Compared to conventional toolboxes, the superiority of BEN is illustrated by its robustness, accuracy, and generalizability. Our proposed method not only provides a generalized solution for extracting brain tissue across species but also significantly improves the accuracy of atlas registration, thereby benefiting the downstream processing tasks. As a novel fully automated deep-learning method, BEN is designed as an open-source software to enable high-throughput processing of neuroimaging data across species in preclinical and clinical applications.

neuroscience↗

bioRxiv2022

MemBrain: A Deep Learning-aided Pipeline for Automated Detection of Membrane Proteins in Cryo-electron Tomograms

Background and ObjectiveCryo-electron tomography (cryo-ET) is an imaging technique that enables 3D visualization of the native cellular environment at sub-nanometer resolution, providing unpreceded insights into the molecular organization of cells. However, cryo-electron tomograms suffer from low signal-to-noise ratios and anisotropic resolution, which makes subsequent image analysis challenging. In particular, the automated detection of membrane-embedded proteins is a problem still lacking satisfactory solutions. MethodsWe present MemBrain - a new deep learning-based pipeline that automatically detects membrane-bound protein complexes in cryo-electron tomograms. After subvolumes are sampled along a segmented membrane, each subvolume is assigned a score using a convolutional neural network (CNN), and protein positions are extracted by a clustering algorithm. Incorporating rotational subvolume normalization and using a tiny receptive field simplify the task of protein detection and thus facilitate the network training. ResultsMemBrain requires only a small quantity of training labels and achieves excellent performance with only a single annotated membrane (F1 score: 0.88). A detailed evaluation shows that our fully trained pipeline outperforms existing classical computer vision-based and CNN-based approaches by a large margin (F1 score: 0.92 vs. max. 0.63). Furthermore, in addition to protein center positions, MemBrain can determine protein orientations, which has not been implemented by any existing CNN-based method to date. We also show that a pre-trained MemBrain program generalizes to tomograms acquired using different cryo-ET methods and depicting different types of cells. ConclusionsMemBrain is a powerful and label-efficient tool for the detection of membrane protein complexes in cryo-ET data, with the potential to be used in a wide range of biological studies. It is generalizable to various kinds of tomograms, making it possible to use pretrained models for different tasks. Its efficiency in terms of required annotations also allows rapid training and fine-tuning of models. The corresponding code, pretrained models, and instructions for operating the MemBrain program can be found at: https://github.com/CellArchLab/MemBrain

bioinformatics↗

bioRxiv2021

Transcriptomic Profiling Reveals Cancer-Associated Fibroblasts as Potential Targets for the Prognosis and Treatment of Cervical Squamous Cell Carcinoma

Effective treatment of advanced invasive cervical cancer remains challenging nowadays. Herein, single-nucleus RNA sequencing (snRNA-seq) and SpaTial Enhanced REsolution Omics-sequencing (Stereo-seq) technology are used to investigate the immunological microenvironment of cervical squamous cell carcinoma (CSCC), a major type of cervical cancers. The expression levels of most immune checkpoint genes in tumor and inflammation areas of CSCC were not significantly higher than those in the non-cancer samples except for LGALS9 and IDO1. Stronger signals of CD56+ NK cells and immature dendritic cells are found in the hypermetabolic tumor areas, while more eosinophils, immature B cells, and Treg cells are found in the hypometabolic tumor areas. Moreover, a cluster of cancer-associated fibroblasts (CAFs) are identified around some tumors, which highly expressed ACTA2, POSTN, ITGB4, and FAP. The CAFs might support the growth and metastasis of tumors by inhibiting lymphocyte infiltration and remodeling the tumor extracellular matrix. Furthermore, CAFs are associated with poorer survival probability in CSCC patients and might be present in a small fraction ([~]20%) of advanced cancer patients. Collectively, these findings might enhance understanding of the CSCC immunological microenvironment and shed some light on the treatment of advanced CSCC.

cancer biology↗

bioRxiv2021

Extrasynaptic NMDA receptors bidirectionally modulate intrinsic excitability of inhibitory neurons

The NMDA subtype glutamate receptors (NMDARs) play important roles in both physiological and pathological processes in the brain. Comparing to their critical roles in synaptic modifications and excitotoxicity in the excitatory neurons, much less is understood about the functional contributions of NMDARs to the inhibitory/GABAergic neurons. By using selective NMDAR inhibitors and potentiators, we here show that NMDARs bi-directionally modulate the intrinsic excitability (defined as spontaneous/evoked spiking activity and EPSP-spike coupling) in the inhibitory/GABAergic neurons. This modulation depends on GluN2C/2D-but not GluN2A/2B-containing NMDARs. We further show that NMDAR modulator EU1794-4 mostly enhances extrasynaptic NMDAR activity, and by using it we demonstrate a significant contribution of extrasynaptic NMDARs to the modulation of intrinsic excitability in the inhibitory neurons. Altogether, this bidirectional modulation of intrinsic excitability reveals a previously less appreciated importance of NMDARs in the second-to-second functioning of inhibitory/GABAergic neurons.

neuroscience↗