Abstract Liver metastasis, the leading cause of colorectal cancer mortality, exhibits a highly heterogeneous and suppressive immune microenvironment. Here, we sequenced 97 matched samples by using single-cell RNA sequencing and spatial transcriptomics. Strikingly, the metastatic microenvironment underwent remarkable spatial reprogramming of immunosuppressive cells such as MRC1+ CCL18+ M2-like macrophages. We further developed scMetabolism, a computational pipeline for quantifying single-cell metabolism, and observed that those macrophages harbored enhanced metabolic activity. Interestingly, neoadjuvant chemotherapy could block this status and restore the antitumor immune balance in responsive patients, whereas the nonresponsive patients deteriorated into a more suppressive one. Our work described the immune evolution of metastasis and uncovered the black box of how tumors respond to neoadjuvant chemotherapy. Significance: We present a single-cell and spatial atlas of colorectal liver metastasis and found the highly metabolically activated MRC1+ CCL18+ M2-like macrophages in metastatic sites. Efficient neoadjuvant chemotherapy can slow down such metabolic activation, raising the possibility to target metabolism pathways in metastasis. This article is highlighted in the In This Issue feature, p. 1
Abstract STUDY QUESTION Do distinct subpopulations of decidual stromal cells (DSCs) exist and if so, are given subpopulations enriched in recurrent miscarriage (RM)? SUMMARY ANSWER Three subpopulations of DSCs were identified from which inflammatory DSCs (iDSCs) and glycolytic DSCs (glyDSCs) are significantly enriched in RM, with implicated roles in driving decidual inflammation and immune dysregulation. WHAT IS KNOWN ALREADY DSCs play crucial roles in establishing and maintaining a successful pregnancy; dysfunction of DSCs has been considered as one of the key reasons for the development of RM. STUDY DESIGN, SIZE, DURATION We collected 15 early decidual samples from five healthy donors (HDs) and ten RM patients to perform single-cell RNA sequencing (scRNA-seq). A total of 43 RM patients and 37 HDs were enrolled in the validation cohort. PARTICIPANTS/MATERIALS, SETTING, METHODS Non-immune cells and immune cells of decidual tissues were sorted by flow cytometry to perform scRNA-seq. We used tissue microarrays (TMA) to validate three distinct subpopulations of DSCs. The expression of inflammatory and glycolytic proteins by DSCs was validated by immunohistochemistry (IHC) and multiplex immunohistochemistry (mIHC). Different subsets of decidual NK (dNK) cells and macrophages were also validated by multicolor flow cytometry and mIHC. Cell ligand–receptor and spatial analyses between DSCs and immune cells were analyzed by mIHC. MAIN RESULTS AND THE ROLE OF CHANCE We classify the DSCs into three subtypes based on scRNA-seq data: myofibroblastic (myDSCs), inflammatory (iDSCs) and glycolytic (glyDSCs), with the latter two being significantly enriched in RM patients. The distribution patterns of DSC subtypes in the RM and HD groups were validated by mIHC. Single-cell analyses indicate that the differentiation of iDSCs and glyDSCs may be coupled with the degrees of hypoxia. Consequently, we propose a pathological model in which a vicious circle is formed and fueled by hypoxic stress, uncontrolled inflammation and aberrant glycolysis. Furthermore, our results show that the inflammatory SPP1+ macrophages and CD18+ dNK cells are preferentially increased in the decidua of RM patients. Cell ligand–receptor and mIHC spatial analyses uncovered close interactions between pathogenic DSCs and inflammatory SPP1+ macrophages and CD18+ NK cells in RM patients. LARGE SCALE DATA The raw single-cell sequence data reported in this paper were deposited at the National Omics Data Encyclopedia (www.biosino.org), under the accession number OEP002901. LIMITATIONS, REASONS FOR CAUTION The number of decidual samples for scRNA-seq was limited and in-depth functional studies on DSCs are warranted in future studies. WIDER IMPLICATIONS OF THE FINDINGS Identification of three DSC subpopulations opens new avenues for further investigation of their roles in RM patients. STUDY FUNDING/COMPETING INTEREST(S) This study was supported by the Strategic Priority Research Program (No. XDB29030302), Frontier Science Key Research Project (QYZDB-SSW-SMC036), Chinese Academy of Sciences; National Key Research and Development Program of China (2021YFE0200600), National Natural Science Foundation of China (No. 31770960), Shanghai Municipal Science and Technology Major Project (No. 2019SHZDZX02, HS2021SHZX001), and Shanghai Committee of Science and Technology (17411967800). All authors report no conflict of interest.
Abstract Background Single-cell RNA sequencing (scRNA-seq) is the most widely used technique to obtain gene expression profiles from complex tissues. Cell subsets and developmental states are often identified via differential gene expression patterns. Most of the single-cell tools utilized highly variable genes to annotate cell subsets and states. However, we have discovered that a group of genes, which sensitively respond to environmental stimuli with high coefficients of variation (CV), might impose overwhelming influences on the cell type annotation. Result In this research, we developed a method, based on the CV-rank and Shannon entropy, to identify these noise genes, and termed them as “sensitive genes”. To validate the reliability of our methods, we applied our tools in 11 single-cell data sets from different human tissues. The results showed that most of the sensitive genes were enriched pathways related to cellular stress response. Furthermore, we noticed that the unsupervised result was closer to the ground-truth cell labels, after removing the sensitive genes detected by our tools. Conclusion Our study revealed the prevalence of stochastic gene expression patterns in most types of cells, compared the differences among cell marker genes, housekeeping genes (HK genes), and sensitive genes, demonstrated the similarities of functions of sensitive genes in various scRNA-seq data sets, and improved the results of unsupervised clustering towards the ground-truth labels. We hope our method would provide new insights into the reduction of data noise in scRNA-seq data analysis and contribute to the development of better scRNA-seq unsupervised clustering algorithms in the future.
Objectives A population of atypical memory B cells (AtMs) are greatly expanded in patients with active lupus, but their generation and pathophysiological roles are poorly defined. The aim of this study was to comprehensively characterise lupus AtMs with a purpose to identify therapeutic clues to target this B cell population in lupus. Methods Peripheral B cell subsets were measured by flow cytometry. Sorting-purified B cell subsets were subject to RNA sequencing and functional studies. Plasma cytokines and secreted immunoglobulins were detected by Luminex or ELISA. In situ renal B cells were detected by multiplexed immunohistochemistry. Results CD24 − CD20 hi AtMs were strongly increased in two Chinese cohorts of patients with treatment-naïve lupus. Gene expression profile indicated that B cell signalling and activation, lipid/saccharide metabolism and endocytosis pathways were abnormally upregulated in lupus AtMs. In addition, the mammalian target of rapamycin complex 1 (mTORC1) pathway was remarkably activated in lupus AtMs, and blocking mTORC1 signalling by rapamycin abolished the generation of T-bet + B cells and terminal differentiation of lupus AtMs. Furthermore, lupus AtMs displayed a dysfunctional phenotype, underwent accelerated apoptosis, poorly co-stimulated T cells and produced proinflammatory cytokines. Interestingly, lupus AtMs were in a paradoxically differentiated status with markers pro and against terminal differentiation and enriched with antinucleosome reactivity. Finally, AtMs were accumulated in the kidneys of patients with lupus nephritis and associated with disease severity. Conclusions These findings demonstrated that mTORC1-overactivated lupus AtMs are abnormally differentiated with metabolic and functional dysregulations. Inhibiting mTORC1 signalling might be an attractive option to target AtMs and to improve therapeutic effectiveness in patients with lupus.
The peripheral B cell compartment is heavily disturbed in systemic lupus erythematosus (SLE), but whether B cells develop aberrantly in the bone marrow (BM) is largely unknown.
Fibroblast-like synoviocytes (FLSs) contribute to inflammation and joint damage in rheumatoid arthritis (RA). However, the regulatory mechanisms of FLSs in relapse and remission of RA remain unknown. Identifying FLS heterogeneity and their underlying pathogenic roles may lead to discovering novel disease-modifying antirheumatic drugs.
The deep-ocean temperature measurement demands sub-millikelvin precision of approximately 1–3 °C. Performance of negative temperature coefficient (NTC) thermistors is significantly dependent on calibration methods and equations. In this study, six pre-aged NTC thermistors were calibrated in a water bath using a comparison method. The primary standard triple point of water validated thermistor stability. Performances of six calibration equations using 11 combinations of calibration points were evaluated. Fitting lines and interpolation residuals of the calibration equations were compared. The fitting line deviation was within 0.23 mK, excluding the Basic equation that reached 0.65 mK. The average interpolation residuals for Hoge-1 and Basic equations are 0.15 mK and 0.35 mK, respectively. It was demonstrated that Hoge-1 and Basic equations are sufficient and that the third (fourth) order equations are unnecessary in deep-ocean temperature measurements. Both Hoge-1 and Basic equations demonstrated capacity to extrapolate 0.5 K within 1 mK error.
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