DNASE1L3-expressing dendritic cells promote CD8 T cell function and anti-PD-(L)1 therapy efficacy by degrading neutrophil extracellular traps.

CD8 T cell exclusion and dysfunction in the tumor microenvironment (TME) are among the most challenging obstacles for anti-PD-(L)1 therapy. Here, we report that tumor-infiltrating dendritic cell (DC)-specific expression of the deoxyribonuclease, DNASE1L3, is positively correlated with favorable outcomes of anti-PD-(L)1 treatment in cancer patients. DNASE1L3 conditional knockout in DCs leads to enhanced tumor growth and diminishes anti-PD-L1 therapeutic efficacy by impairing infiltration and effector functions of CD8 T cells.

HHAvatar: Gaussian Head Avatar with Dynamic Hairs.

Creating high-fidelity 3D head avatars has always been a research hotspot, but it remains a great challenge under lightweight sparse view setups. In this paper, we propose HHAvatar represented by controllable 3D Gaussians for high-fidelity head avatar with dynamic hair modeling. We first use 3D Gaussians to represent the appearance of the head, and then jointly optimize neutral 3D Gaussians and a fully learned MLP-based deformation field to capture complex expressions.

An optical model for evaluating the influence of microstructures on zirconia translucency.

Objectives: Pores and tetragonal phase crystallites are the two main scatterers in yttria-stabilized zirconia (YSZ) ceramics. Previous optical models have often overestimated the proportion of the tetragonal phase and lacked accurate characterization of pore distribution. The aim of this study was therefore to construct a transmission-scattering model for zirconia, to separately calculate and analyze the contributions of pores and tetragonal phase crystallites to translucency.

Predicting enamel depth distribution of maxillary teeth based on intraoral scanning: A machine learning study.

Purpose: Measuring enamel depth distribution (EDD) is of great importance for preoperative design of tooth preparations, restorative aesthetic preview and monitoring enamel wear. But, currently there are no non-invasive methods available to efficiently obtain EDD. This study aimed to develop a machine learning (ML) framework to achieve noninvasive and radiation-free EDD predictions with intraoral scanning (IOS) images.

Bone metastases diminish extraosseous response to checkpoint blockade immunotherapy through osteopontin-producing osteoclasts.

Bone metastatic lesions typically associate with suboptimal responses to immune checkpoint blockade (ICB) therapies. In this study, we observed that across multiple clinical cohorts and a variety of mouse models, the presence of osseous metastases induces ICB resistance in extraosseous tumors. Mechanistically, this long-distance communication is mediated by osseous tumor-conditioned osteoclasts producing osteopontin (OPN).

Supercharging CAR-T cells through transcriptional and epigenetic armoring.

Inspired by the remarkable success of CAR-T therapy in hematologic malignancies, research is increasingly focused on adapting this treatment for solid tumors. However, CAR-T efficacy remains limited due to its exhaustion and shortened persistence. Transcription factors and epigenetic modifications play pivotal roles in modulating T cell differentiation and functionality, which have been leveraged in numerous strategies to promote the formation of long-lasting memory cells with stem-like properties and supercharging CAR-T performance.

Neoadjuvant anti-4-1BB confers protection against spontaneous metastasis through low-affinity intratumor CD8 T cells in triple-negative breast cancer.

Neoadjuvant immunotherapy seeks to harness the primary tumor as a source of relevant tumor antigens to enhance systemic anti-tumor immunity through improved immunological surveillance. Despite having revolutionized the treatment of patients with high-risk early-stage triple-negative breast cancer (TNBC), a significant portion of patients remain unresponsive and succumb to metastatic recurrence post-treatment. Here, we found that optimally scheduled neoadjuvant administration of anti-4-1BB monotherapy was able to counteract metastases and prolong survival following surgical resection.

B-Lightning: using bait genes for marker gene hunting in single-cell data with complex heterogeneity.

In single-cell studies, cells can be characterized with multiple sources of heterogeneity (SOH) such as cell type, developmental stage, cell cycle phase, activation state, and so on. In some studies, many nuisance SOH are of no interest, but may confound the identification of the SOH of interest, and thus affect the accurate annotate the corresponding cell subpopulations. In this paper, we develop B-Lightning, a novel and robust method designed to identify marker genes and cell subpopulations corresponding to an SOH (e.

IL-1 signaling in aging and cancer: An inflammaging feedback loop unveiled.

In a Science paper, Park et al. identified interleukin (IL)-1α as a key driver of positive feedback in inflammaging, linking aging-associated downregulation of DNMT3A to increased IL-1α production in lung myeloid cells. This triggers emergency myelopoiesis in the bone marrow, amplifying myeloid-mediated intratumoral immunosuppression for tumor progression in aged mice.

Aberrant cytoplasmic expression of UHRF1 restrains the MHC-I-mediated anti-tumor immune response.

Immunotherapy successfully complements traditional cancer treatment. However, primary and acquired resistance might limit efficacy. Reduced antigen presentation by MHC-I has been identified as potential resistance factor.

A machine learning based approach to standardizing tooth color and shade recommendations.

Statement Of Problem: Achieving the precise reproduction of tooth color is pivotal in esthetic restorations. However, existing visual and instrumental shade matching methods, each with inherent limitations, have often resulted in inconsistent color communication and subsequently suboptimal esthetic results in clinical practice.

Purpose: The purpose of this in vitro study was to evaluate a 2-tier color correction strategy to accurately acquire standardized tooth color and to provide shade recommendations for esthetic restorations.

Deciphering tumour microenvironment and elucidating the origin of cancer cells in ovarian clear cell carcinoma.

Ovarian clear cell carcinoma (CCC) has an East Asian preponderance. It is associated with endometriosis, a benign condition where endometrial (inner lining of the uterus) tissue is found outside the uterus and on the peritoneal surface, in the abdominal or pelvic space. CCC is relatively more resistant to conventional chemotherapy compared to other ovarian cancer subtypes and is associated with a poorer prognosis.

SifiNet: a robust and accurate method to identify feature gene sets and annotate cells.

SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods.

Precise tooth design using deep learning-based templates.

Objectives: In prosthodontic procedures, traditional computer-aided design (CAD) is often time-consuming and lacks accuracy in shape restoration. In this study, we combined implicit template and deep learning (DL) to construct a precise neural network for personalized tooth defect restoration.

Methods: Ninety models of right maxillary central incisor (80 for training, 10 for validation) were collected.

Breaking NGF-TrkA immunosuppression in melanoma sensitizes immunotherapy for durable memory T cell protection.

Melanoma cells, deriving from neuroectodermal melanocytes, may exploit the nervous system's immune privilege for growth. Here we show that nerve growth factor (NGF) has both melanoma cell intrinsic and extrinsic immunosuppressive functions. Autocrine NGF engages tropomyosin receptor kinase A (TrkA) on melanoma cells to desensitize interferon γ signaling, leading to T and natural killer cell exclusion.

Branched-Chain Amino Acid Accumulation Fuels the Senescence-Associated Secretory Phenotype.

The essential branched-chain amino acids (BCAAs) leucine, isoleucine, and valine play critical roles in protein synthesis and energy metabolism. Despite their widespread use as nutritional supplements, BCAAs' full effects on mammalian physiology remain uncertain due to the complexities of BCAA metabolic regulation. Here a novel mechanism linking intrinsic alterations in BCAA metabolism is identified to cellular senescence and the senescence-associated secretory phenotype (SASP), both of which contribute to organismal aging and inflammation-related diseases.

Single-nucleus multiomic mapping of mA methylomes and transcriptomes in native populations of cells with sn-m6A-CT.

N-methyladenosine (mA) RNA modification plays important roles in the governance of gene expression and is temporally regulated in different cell states. In contrast to global mA profiling in bulk sequencing, single-cell technologies for analyzing mA heterogeneity are not extensively established. Here, we developed single-nucleus m6A-CUT&Tag (sn-m6A-CT) for simultaneous profiling of mA methylomes and transcriptomes within a single nucleus using mouse embryonic stem cells (mESCs).

SifiNet: A robust and accurate method to identify feature gene sets and annotate cells.

SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods.

Antagonizing the irreversible thrombomodulin-initiated proteolytic signaling alleviates age-related liver fibrosis via senescent cell killing.

Cellular senescence is a stress-induced, stable cell cycle arrest phenotype which generates a pro-inflammatory microenvironment, leading to chronic inflammation and age-associated diseases. Determining the fundamental molecular pathways driving senescence instead of apoptosis could enable the identification of senolytic agents to restore tissue homeostasis. Here, we identify thrombomodulin (THBD) signaling as a key molecular determinant of the senescent cell fate.

Neural mechanism underlying depressive-like state associated with social status loss.

Downward social mobility is a well-known mental risk factor for depression, but its neural mechanism remains elusive. Here, by forcing mice to lose against their subordinates in a non-violent social contest, we lower their social ranks stably and induce depressive-like behaviors. These rank-decline-associated depressive-like behaviors can be reversed by regaining social status.

CD200 cytotoxic T lymphocytes in the tumor microenvironment are crucial for efficacious anti-PD-1/PD-L1 therapy.

Anti-PD-1/PD-L1 therapy, either by anti-PD-1 antibody or anti-PD-L1 antibody, has efficacy by reinvigorating tumor-infiltrating CD8 T cells in a subset of patients with cancer, but it has unequal effects on heterogeneous CD8 T cell populations. Hence, the subset crucial to efficacious PD-1 blockade therapy remains elusive. Here, we found an increase in tumor-infiltrating CD200 cytotoxic T lymphocytes (CTLs) upon PD-1/PD-L1 blockade, with higher proportions of CD200 T cells positively related to a favorable clinical outcome to anti-PD-1/PD-L1 therapy in three independent cohorts of patients with cancer.

Clustering Deviation Index (CDI): a robust and accurate internal measure for evaluating scRNA-seq data clustering.

Most single-cell RNA sequencing (scRNA-seq) analyses begin with cell clustering; thus, the clustering accuracy considerably impacts the validity of downstream analyses. In contrast with the abundance of clustering methods, the tools to assess the clustering accuracy are limited. We propose a new Clustering Deviation Index (CDI) that measures the deviation of any clustering label set from the observed single-cell data.