Proper development of mucosal immunity is critical for human health. Over the past decade, it has become evident that in humans, this process begins in utero. However, there are limited data on the unique features and functions of fetal mucosal immune cells. To address this gap, we integrated several single-cell ribonucleic acid sequencing datasets of the human small intestine (SI) to create an SI transcriptional atlas throughout the human life span, ranging from the first trimester to adulthood, with a focus on immune cells. Fetal SI displayed a complex immune landscape comprising innate and adaptive immune cells that exhibited distinct transcriptional programs from postnatal samples, especially compared with pediatric and adult samples. We identified shifts in myeloid populations across gestation and progression of memory T-cell states throughout the human lifespan. In particular, there was a marked shift of memory T cells from those with stem-like properties in the fetal samples to fully differentiated cells with a high expression of activation and effector function genes in adult samples, with neonatal samples containing both features. Finally, we demonstrate that the SI developmental atlas can be used to elucidate improper trajectories linked to mucosal diseases by implicating developmental abnormalities underlying necrotizing enterocolitis, a severe intestinal complication of prematurity. Collectively, our data provide valuable resources and important insights into intestinal immunity that will facilitate regenerative medicine and disease understanding.
Immune reactions to medical implants often lead to encapsulation by fibrotic tissue and impaired device function. This process is thought to initiate by protein adsorption, which enables immune cells to attach and mount an inflammatory response. Previously, several antifibrotic materials have been either designed to reduce protein adsorption or discovered via high-throughput screens (HTS) to favorably regulate inflammation.
Introduction: Low back pain (LBP) is a significant global health burden, with variable treatment outcomes and an unclear underlying molecular mechanism. Effective prediction of treatment responses remains a challenge. In this study, we aimed to develop gene signature-based machine learning models using transcriptomic data from peripheral immune cells to predict treatment outcomes in patients with LBP.
Background: Gastric cancer (GC) shows limited response to immune checkpoint inhibitors due to its complex tumor immune microenvironment (TIME). This study explores the functions of various immune cells in the complex TIME in GC.
Methods: We assessed CD8 + T-cell infiltration of GC tissues by immunohistochemistry, and performed single-cell RNA sequencing (scRNA-seq) of tumor and normal tissues from 34 patients with GC.
Background: Intrauterine adhesion (IUA) is a common cause of clinically refractory infertility, and there exists significant heterogeneity in the treatment outcomes among IUA patients with the similar severity after transcervical resection of adhesion(TCRA). The underlying mechanism of different treatment outcomes occur remains elusive, and the precise contribution of various cell subtypes in this process remains uncertain.
Results: Here, we performed single-cell transcriptome sequencing on 10 human endometrial samples to establish a single-cell atlas differences between patients who responded to estrogen therapy and those who did not.
Tumor-initiating cells (TICs) are particularly efficient at evading detection and elimination by the human immune system. Recent data from Yang and collaborators demonstrate that - at least in preclinical hepatocellular carcinoma models - the immunological privilege of CD49f TICs can be limited by targeting CD155, resulting in restored sensitivity to immune checkpoint inhibitors.
