Insights Gained from Single-Cell Analysis of Immune Cells in the Tumor Microenvironment.

Understanding tumor immune microenvironments is critical for identifying immune modifiers of cancer progression and developing cancer immunotherapies. Recent applications of single-cell RNA sequencing (scRNA-seq) in dissecting tumor microenvironments have brought important insights into the biology of tumor-infiltrating immune cells, including their heterogeneity, dynamics, and potential roles in both disease progression and response to immune checkpoint inhibitors and other immunotherapies. This review focuses on the advances in knowledge of tumor immune microenvironments acquired from scRNA-seq studies across multiple types of human tumors, with a particular emphasis on the study of phenotypic plasticity and lineage dynamics of immune cells in the tumor environment. We also discuss several imminent questions emerging from scRNA-seq observations and their potential solutions on the horizon.

In vivo CRISPR screens identify the E3 ligase Cop1 as a modulator of macrophage infiltration and cancer immunotherapy target.

Despite remarkable clinical efficacy of immune checkpoint blockade (ICB) in cancer treatment, ICB benefits for triple-negative breast cancer (TNBC) remain limited. Through pooled in vivo CRISPR knockout (KO) screens in syngeneic TNBC mouse models, we found that deletion of the E3 ubiquitin ligase Cop1 in cancer cells decreases secretion of macrophage-associated chemokines, reduces tumor macrophage infiltration, enhances anti-tumor immunity, and strengthens ICB response. Transcriptomics, epigenomics, and proteomics analyses revealed that Cop1 functions through proteasomal degradation of the C/ebpδ protein. The Cop1 substrate Trib2 functions as a scaffold linking Cop1 and C/ebpδ, which leads to polyubiquitination of C/ebpδ. In addition, deletion of the E3 ubiquitin ligase Cop1 in cancer cells stabilizes C/ebpδ to suppress expression of macrophage chemoattractant genes. Our integrated approach implicates Cop1 as a target for improving cancer immunotherapy efficacy in TNBC by regulating chemokine secretion and macrophage infiltration in the tumor microenvironment.

A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells.

Tumor-infiltrating myeloid cells (TIMs) are key regulators in tumor progression, but the similarity and distinction of their fundamental properties across different tumors remain elusive. Here, by performing a pan-cancer analysis of single myeloid cells from 210 patients across 15 human cancer types, we identified distinct features of TIMs across cancer types. Mast cells in nasopharyngeal cancer were found to be associated with better prognosis and exhibited an anti-tumor phenotype with a high ratio of TNF+/VEGFA+ cells. Systematic comparison between cDC1- and cDC2-derived LAMP3+ cDCs revealed their differences in transcription factors and external stimulus. Additionally, pro-angiogenic tumor-associated macrophages (TAMs) were characterized with diverse markers across different cancer types, and the composition of TIMs appeared to be associated with certain features of somatic mutations and gene expressions. Our results provide a systematic view of the highly heterogeneous TIMs and suggest future avenues for rational, targeted immunotherapies.

Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer.

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for defining cellular diversity in tumors, but its application toward dissecting mechanisms underlying immune-modulating therapies is scarce. We performed scRNA-seq analyses on immune and stromal populations from colorectal cancer patients, identifying specific macrophage and conventional dendritic cell (cDC) subsets as key mediators of cellular cross-talk in the tumor microenvironment. Defining comparable myeloid populations in mouse tumors enabled characterization of their response to myeloid-targeted immunotherapy. Treatment with anti-CSF1R preferentially depleted macrophages with an inflammatory signature but spared macrophage populations that in mouse and human expresses pro-angiogenic/tumorigenic genes. Treatment with a CD40 agonist antibody preferentially activated a cDC population and increased Bhlhe40+ Th1-like cells and CD8+ memory T cells. Our comprehensive analysis of key myeloid subsets in human and mouse identifies critical cellular interactions regulating tumor immunity and defines mechanisms underlying myeloid-targeted immunotherapies currently undergoing clinical testing.

Notch signaling regulates macrophage-mediated inflammation in metabolic dysfunction-associated steatotic liver disease.

The liver macrophage population comprises resident Kupffer cells (KCs) and monocyte-derived macrophages with distinct pro- or anti-inflammatory properties that affect the severity and course of liver diseases. The mechanisms underlying macrophage differentiation and functions in metabolic dysfunction-associated steatotic liver disease and/or steatohepatitis (MASLD/MASH) remain mostly unknown. Using single-cell RNA sequencing (scRNA-seq) and fate mapping of hepatic macrophage subpopulations, we unraveled the temporal and spatial dynamics of distinct monocyte and monocyte-derived macrophage subsets in MASH. We revealed a crucial role for the Notch-Recombination signal binding protein for immunoglobulin kappa J region (RBPJ) signaling pathway in controlling the monocyte-to-macrophage transition, with Rbpj deficiency blunting inflammatory macrophages and monocyte-derived KC differentiation and conversely promoting the emergence of protective Ly6Clo monocytes. Mechanistically, Rbpj deficiency promoted lipid uptake driven by elevated CD36 expression in Ly6Clo monocytes, enhancing their protective interactions with endothelial cells. Our findings uncover the crucial role of Notch-RBPJ signaling in monocyte-to-macrophage transition and will aid in the design of therapeutic strategies for MASH treatment.

Dendritic cell type 3 arises from Ly6C+ monocyte-dendritic cell progenitors.

Conventional dendritic cells (cDCs) are professional antigen-presenting cells that control the adaptive immune response. Their subsets and developmental origins have been intensively investigated but are still not fully understood as their phenotypes, especially in the DC2 lineage and the recently described human DC3s, overlap with monocytes. Here, using LEGENDScreen to profile DC vs. monocyte lineages, we found sustained expression of FLT3 and CD45RB through the whole DC lineage, allowing DCs and their precursors to be distinguished from monocytes. Using fate mapping models, single-cell RNA sequencing and adoptive transfer, we identified a lineage of murine CD16/32+CD172a+ DC3, distinct from DC2, arising from Ly6C+ monocyte-DC progenitors (MDPs) through Lyz2+Ly6C+CD11c- pro-DC3s, whereas DC2s develop from common DC progenitors (CDPs) through CD7+Ly6C+CD11c+ pre-DC2s. Corresponding DC subsets, developmental stages, and lineages exist in humans. These findings reveal DC3 as a DC lineage phenotypically related to but developmentally different from monocytes and DC2s.

Single-Cell Sequencing of Peripheral Mononuclear Cells Reveals Distinct Immune Response Landscapes of COVID-19 and Influenza Patients.

The coronavirus disease 2019 (COVID-19) pandemic poses a current world-wide public health threat. However, little is known about its hallmarks compared to other infectious diseases. Here, we report the single-cell transcriptional landscape of longitudinally collected peripheral blood mononuclear cells (PBMCs) in both COVID-19- and influenza A virus (IAV)-infected patients. We observed increase of plasma cells in both COVID-19 and IAV patients and XIAP associated factor 1 (XAF1)-, tumor necrosis factor (TNF)-, and FAS-induced T cell apoptosis in COVID-19 patients. Further analyses revealed distinct signaling pathways activated in COVID-19 (STAT1 and IRF3) versus IAV (STAT3 and NFκB) patients and substantial differences in the expression of key factors. These factors include relatively increase of interleukin (IL)6R and IL6ST expression in COVID-19 patients but similarly increased IL-6 concentrations compared to IAV patients, supporting the clinical observations of increased proinflammatory cytokines in COVID-19 patients. Thus, we provide the landscape of PBMCs and unveil distinct immune response pathways in COVID-19 and IAV patients.

Peptide sequencing based on host-guest interaction-assisted nanopore sensing.

Direct protein sequencing technologies with improved sensitivity and throughput are still needed. Here, we propose an alternative method for peptide sequencing based on enzymatic cleavage and host-guest interaction-assisted nanopore sensing. We serendipitously discovered that the identity of any proteinogenic amino acid in a particular position of a phenylalanine-containing peptide could be determined via current blockage during translocation of the peptide through α-hemolysin nanopores in the presence of cucurbit[7]uril. Building upon this, we further present a proof-of-concept demonstration of peptide sequencing by sequentially cleaving off amino acids from C terminus of a peptide with carboxypeptidases, and then determining their identities and sequence with a peptide probe in nanopore. With future optimization, our results point to a different way of nanopore-based protein sequencing.

Alveolar macrophage function is impaired following inhalation of berry e-cigarette vapor.

In the lower respiratory tract, the alveolar spaces are divided from the bloodstream and the external environment by only a few microns of interstitial tissue. Alveolar macrophages (AMs) defend this delicate mucosal surface from invading infections by regularly patrolling the site. AMs have three behavior modalities to achieve this goal: extending cell protrusions to probe and sample surrounding areas, squeezing the whole cell body between alveoli, and patrolling by moving the cell body around each alveolus. In this study, we found Rho GTPase, cell division control protein 42 (CDC42) expression significantly decreased after berry-flavored e-cigarette (e-cig) exposure. This shifted AM behavior from squeezing to probing. Changes in AM behavior led to a reduction in the clearance of inhaled bacteria, Pseudomonas aeruginosa. These findings shed light on pathways involved in AM migration and highlight the harmful impact of e-cig vaping on AM function.

Zebrafish mylipb attenuates antiviral innate immunity through two synergistic mechanisms targeting transcription factor irf3.

IFN regulatory factor 3 (IRF3) is the transcription factor crucial for the production of type I IFN in viral defence and inflammatory responses. The activity of IRF3 is strictly modulated by post-translational modifications (PTMs) to effectively protect the host from infection while avoiding excessive immunopathology. Here, we report that zebrafish myosin-regulated light chain interacting protein b (mylipb) inhibits virus-induced type I IFN production via two synergistic mechanisms: induction of autophagic degradation of irf3 and reduction of irf3 phosphorylation. In vivo, mylipb-null zebrafish exhibit reduced lethality and viral mRNA levels compared to controls. At the cellular level, overexpression of mylipb significantly reduces cellular antiviral capacity, and promotes viral proliferation. Mechanistically, mylipb associates with irf3 and targets Lys 352 to increase K6-linked polyubiquitination, dependent on its E3 ubiquitin ligase activity, leading to autophagic degradation of irf3. Meanwhile, mylipb acts as a decoy substrate for the phosphokinase tbk1 to attenuate irf3 phosphorylation and cellular antiviral responses independent of its enzymatic activity. These findings support a critical role for zebrafish mylipb in the limitation of antiviral innate immunity through two synergistic mechanisms targeting irf3.

Correction: TDP-43 and other hnRNPs regulate cryptic exon inclusion of a key ALS/FTD risk gene, UNC13A.

[This corrects the article DOI: 10.1371/journal.pbio.3002028.].

TDP-43 and other hnRNPs regulate cryptic exon inclusion of a key ALS/FTD risk gene, UNC13A.

A major function of TAR DNA-binding protein-43 (TDP-43) is to repress the inclusion of cryptic exons during RNA splicing. One of these cryptic exons is in UNC13A, a genetic risk factor for amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). The accumulation of cryptic UNC13A in disease is heightened by the presence of a risk haplotype located within the cryptic exon itself. Here, we revealed that TDP-43 extreme N-terminus is important to repress UNC13A cryptic exon inclusion. Further, we found hnRNP L, hnRNP A1, and hnRNP A2B1 bind UNC13A RNA and repress cryptic exon inclusion, independently of TDP-43. Finally, higher levels of hnRNP L protein associate with lower burden of UNC13A cryptic RNA in ALS/FTD brains. Our findings suggest that while TDP-43 is the main repressor of UNC13A cryptic exon inclusion, other hnRNPs contribute to its regulation and may potentially function as disease modifiers.

A comprehensive assessment of cell type-specific differential expression methods in bulk data.

Accounting for cell type compositions has been very successful at analyzing high-throughput data from heterogeneous tissues. Differential gene expression analysis at cell type level is becoming increasingly popular, yielding biomarker discovery in a finer granularity within a particular cell type. Although several computational methods have been developed to identify cell type-specific differentially expressed genes (csDEG) from RNA-seq data, a systematic evaluation is yet to be performed. Here, we thoroughly benchmark six recently published methods: CellDMC, CARseq, TOAST, LRCDE, CeDAR and TCA, together with two classical methods, csSAM and DESeq2, for a comprehensive comparison. We aim to systematically evaluate the performance of popular csDEG detection methods and provide guidance to researchers. In simulation studies, we benchmark available methods under various scenarios of baseline expression levels, sample sizes, cell type compositions, expression level alterations, technical noises and biological dispersions. Real data analyses of three large datasets on inflammatory bowel disease, lung cancer and autism provide evaluation in both the gene level and the pathway level. We find that csDEG calling is strongly affected by effect size, baseline expression level and cell type compositions. Results imply that csDEG discovery is a challenging task itself, with room to improvements on handling low signal-to-noise ratio and low expression genes.

A novel statistical method for decontaminating T-cell receptor sequencing data.

The T-cell receptor (TCR) repertoire is highly diverse among the population and plays an essential role in initiating multiple immune processes. TCR sequencing (TCR-seq) has been developed to profile the T cell repertoire. Similar to other high-throughput experiments, contamination can happen during several steps of TCR-seq, including sample collection, preparation and sequencing. Such contamination creates artifacts in the data, leading to inaccurate or even biased results. Most existing methods assume 'clean' TCR-seq data as the starting point with no ability to handle data contamination. Here, we develop a novel statistical model to systematically detect and remove contamination in TCR-seq data. We summarize the observed contamination into two sources, pairwise and cross-cohort. For both sources, we provide visualizations and summary statistics to help users assess the severity of the contamination. Incorporating prior information from 14 existing TCR-seq datasets with minimum contamination, we develop a straightforward Bayesian model to statistically identify contaminated samples. We further provide strategies for removing the impacted sequences to allow for downstream analysis, thus avoiding any need to repeat experiments. Our proposed model shows robustness in contamination detection compared with a few off-the-shelf detection methods in simulation studies. We illustrate the use of our proposed method on two TCR-seq datasets generated locally.

SCIntRuler: guiding the integration of multiple single-cell RNA-seq datasets with a novel statistical metric.

MOTIVATION: The growing number of single-cell RNA-seq (scRNA-seq) studies highlights the potential benefits of integrating multiple datasets, such as augmenting sample sizes and enhancing analytical robustness. Inherent diversity and batch discrepancies within samples or across studies continue to pose significant challenges for computational analyses. Questions persist in practice, lacking definitive answers: Should we use a specific integration method or opt for simply merging the datasets during joint analysis? Among all the existing data integration methods, which one is more suitable in specific scenarios? RESULT: To fill the gap, we introduce SCIntRuler, a novel statistical metric for guiding the integration of multiple scRNA-seq datasets. SCIntRuler helps researchers make informed decisions regarding the necessity of data integration and the selection of an appropriate integration method. Our simulations and real data applications demonstrate that SCIntRuler streamlines decision-making processes and facilitates the analysis of diverse scRNA-seq datasets under varying contexts, thereby alleviating the complexities associated with the integration of heterogeneous scRNA-seq datasets. AVAILABILITY AND IMPLEMENTATION: The implementation of our method is available on CRAN as an open-source R package with a user-friendly manual available: https://cloud.r-project.org/web/packages/SCIntRuler/index.html.

Regional analysis to delineate intrasample heterogeneity with RegionalST.

MOTIVATION: Spatial transcriptomics has greatly contributed to our understanding of spatial and intra-sample heterogeneity, which could be crucial for deciphering the molecular basis of human diseases. Intra-tumor heterogeneity, e.g. may be associated with cancer treatment responses. However, the lack of computational tools for exploiting cross-regional information and the limited spatial resolution of current technologies present major obstacles to elucidating tissue heterogeneity. RESULTS: To address these challenges, we introduce RegionalST, an efficient computational method that enables users to quantify cell type mixture and interactions, identify sub-regions of interest, and perform cross-region cell type-specific differential analysis for the first time. Our simulations and real data applications demonstrate that RegionalST is an efficient tool for visualizing and analyzing diverse spatial transcriptomics data, thereby enabling accurate and flexible exploration of tissue heterogeneity. Overall, RegionalST provides a one-stop destination for researchers seeking to delve deeper into the intricacies of spatial transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The implementation of our method is available as an open-source R/Bioconductor package with a user-friendly manual available at https://bioconductor.org/packages/release/bioc/html/RegionalST.html.

Incidence and risk factors of antidepressant withdrawal symptoms: a meta-analysis and systematic review.

Antidepressants are among the most extensively prescribed psychotropic drugs worldwide. Discontinuation induced withdrawal symptoms have been reported for almost all antidepressants. The incidence of antidepressant withdrawal syndrome (AWS) and other characteristics remain unknown. We searched the PubMed, Embase, PsycINFO, MEDLINE, CINAHL, and Cochrane Central Register of Controlled Trials databases from inception to December 31, 2023. Randomized double-blinded trials, longitudinal or cross-sectional studies that reported the incidence and other characteristics of antidepressant withdrawal symptoms were included. The pooled incidence of AWS was calculated by a random effects model. We included 35 studies, of which 2 studies just provided incidence of specific withdrawal symptoms, and 4 studies only described other characteristics. The pooled incidence of AWS from all available studies was 42.9%, from 11 RCTs was 44.4%, in studies in which the treatment duration was mostly 8-12 weeks, which usually appear within 2 weeks, and were generally measured for <4 weeks. The incidence in selective serotonin-norepinephrine reuptake inhibitors was the lowest (29.7%), followed by selective serotonin reuptake inhibitors (45.6%) and tricyclic antidepressants (59.7%), without significant differences (p = 0.221). Treatment duration showed a dose-response to the incidence of AWS (6-12 W: 35.1%, 12-24 W: 42.7%, >24 W: 51.4%). The half-life did not show such a simple dose-dependent relationship. The pooled estimate was robust regardless whether withdrawal symptoms were measured in RCTs or observational studies (including face-to-face and online survey studies). Tapering the dose reduced the incidence of AWS compared with abrupt stoppage (34.5% vs 42.5%), without a significant difference (p = 0.484). Risk factors for withdrawal symptoms included being female, younger, experiencing adverse effects early in treatment, taking higher doses or longer duration of medication, abrupt cessation of drugs, and those with a lower clearance of drugs or with serotonin 1A receptor gene variation. The findings suggest the incidence of AWS are common and some clinical characteristics and risk factors which can help clinicians identify who is at greater risk of experiencing AWS. Discontinuation studies on long-term antidepressant users with long follow-up periods are required in the future.

Extracellular RNA Sensing Mediates Inflammation and Organ Injury in a Murine Model of Polytrauma.

Severe traumatic injury leads to marked systemic inflammation and multiorgan injury. Endogenous drivers such as extracellular nucleic acid may play a role in mediating innate immune response and the downstream pathogenesis. Here, we explored the role of plasma extracellular RNA (exRNA) and its sensing mechanism in inflammation and organ injury in a murine model of polytrauma. We found that severe polytrauma-bone fracture, muscle crush injury, and bowel ischemia-induced a marked increase in plasma exRNA, systemic inflammation, and multiorgan injury in mice. Plasma RNA profiling with RNA sequencing in mice and humans revealed a dominant presence of miRNAs and marked differential expression of numerous miRNAs after severe trauma. Plasma exRNA isolated from trauma mice induced a dose-dependent cytokine production in macrophages, which was almost abolished in TLR7-deficient cells but unchanged in TLR3-deficient cells. Moreover, RNase or specific miRNA inhibitors against the selected proinflammatory miRNAs (i.e., miR-7a-5p, miR-142, let-7j, miR-802, and miR-146a-5p) abolished or attenuated trauma plasma exRNA-induced cytokine production, respectively. Bioinformatic analyses of a group of miRNAs based on cytokine readouts revealed that high uridine abundance (>40%) is a reliable predictor in miRNA mimic-induced cytokine and complement production. Finally, compared with the wild-type, TLR7-knockout mice had attenuated plasma cytokine storm and reduced lung and hepatic injury after polytrauma. These data suggest that endogenous plasma exRNA of severely injured mice and ex-miRNAs with high uridine abundance prove to be highly proinflammatory. TLR7 sensing of plasma exRNA and ex-miRNAs activates innate immune responses and plays a role in inflammation and organ injury after trauma.

MicroRNAs are enriched at COVID-19 genomic risk regions, and their blood levels correlate with the COVID-19 prognosis of cancer patients infected by SARS-CoV-2.

BACKGROUND: Cancer patients are more susceptible to an aggressive course of COVID-19. Developing biomarkers identifying cancer patients at high risk of COVID-19-related death could help determine who needs early clinical intervention. The miRNAs hosted in the genomic regions associated with the risk of aggressive COVID-19 could represent potential biomarkers for clinical outcomes. PATIENTS AND METHODS: Plasma samples were collected at The University of Texas MD Anderson Cancer Center from cancer patients (N = 128) affected by COVID-19. Serum samples were collected from vaccinated healthy individuals (n = 23) at the Municipal Clinical Emergency Teaching Hospital in Timisoara, Romania. An in silico positional cloning approach was used to identify the presence of miRNAs at COVID-19 risk-associated genomic regions: CORSAIRs (COvid-19 RiSk AssocIated genomic Regions). The miRNA levels were measured by RT-qPCR. RESULTS: We found that miRNAs were enriched in CORSAIR. Low plasma levels of hsa-miR-150-5p and hsa-miR-93-5p were associated with higher COVID-19-related death. The levels of hsa-miR-92b-3p were associated with SARS-CoV-2 test positivity. Peripheral blood mononuclear cells (PBMC) increased secretion of hsa-miR-150-5p, hsa-miR-93-5p, and hsa-miR-92b-3p after in vitro TLR7/8- and T cell receptor (TCR)-mediated activation. Increased levels of these three miRNAs were measured in the serum samples of healthy individuals between one and nine months after the second dose of the Pfizer-BioNTech COVID-19 vaccine. SARS-CoV-2 infection of human airway epithelial cells influenced the miRNA levels inside their secreted extracellular vesicles. CONCLUSIONS: MiRNAs are enriched at CORSAIR. Plasma miRNA levels can represent a potential blood biomarker for predicting COVID-19-related death in cancer patients.

Altered hydroxymethylome in the substantia nigra of Parkinson's disease.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, and aging and genetic and environmental exposure can contribute to its pathogenesis. DNA methylation has been suggested to play a pivotal role in neurodevelopment and neurodegenerative diseases. 5-hydroxymethylcytosine (5hmC) is generated through 5-methylcytosine (5mC) oxidization by ten-eleven translocation proteins and is particularly enriched in the brain. Although 5hmC has been linked to multiple neurological disorders, little is known about 5hmC alterations in the substantia nigra of patients with PD. To determine the specific alterations in DNA methylation and hydroxymethylation in PD brain samples, we examined the genome-wide profiles of 5mC and 5hmC in the substantia nigra of patients with PD and Alzheimer's disease (ad). We identified 4119 differentially hydroxymethylated regions (DhMRs) and no differentially methylated regions (DMRs) in the postmortem brains of patients with PD compared with those of controls. These DhMRs were PD-specific when compared with the results of AD. Gene ontology analysis revealed that several signaling pathways, such as neurogenesis and neuronal differentiation, were significantly enriched in PD DhMRs. KEGG enrichment analysis revealed substantial alterations in multiple signaling pathways, including phospholipase D (PLD), cAMP and Rap1. In addition, using a PD Drosophila model, we found that one of the 5hmC-modulated genes, PLD1, modulated α-synuclein toxicity. Our analysis suggested that 5hmC may act as an independent epigenetic marker and contribute to the pathogenesis of PD.