Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma

HMGA1 orchestrates chromatin compartmentalization and sequesters genes into 3D networks coordinating senescence heterogeneity

HMGA1 is an abundant non-histone chromatin protein that has been implicated in embryonic development, cancer, and cellular senescence, but its specific role remains elusive. Here, we combine functional genomics approaches with graph theory to investigate how HMGA1 genomic deposition controls high-order chromatin networks in an oncogene-induced senescence model. While the direct role of HMGA1 in gene activation has been described previously, we find little evidence to support this. Instead, we show that the heterogeneous linear distribution of HMGA1 drives a specific 3D chromatin organization. HMGA1-dense loci form highly interactive networks, similar to, but independent of, constitutive heterochromatic loci. This, coupled with the exclusion of HMGA1-poor chromatin regions, leads to coordinated gene regulation through the repositioning of genes. In the absence of HMGA1, the whole process is largely reversed, but many regulatory interactions also emerge, amplifying the inflammatory senescence-associated secretory phenotype. Such HMGA1-mediated fine-tuning of gene expression contributes to the heterogeneous nature of senescence at the single-cell level. A similar 'buffer' effect of HMGA1 on inflammatory signalling is also detected in lung cancer cells. Our study reveals a mechanism through which HMGA1 modulates chromatin compartmentalization and gene regulation in senescence and beyond.

Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis

Here we demonstrate that the large language model GPT-4 can accurately annotate cell types using marker gene information in single-cell RNA sequencing analysis. When evaluated across hundreds of tissue and cell types, GPT-4 generates cell type annotations exhibiting strong concordance with manual annotations. This capability can considerably reduce the effort and expertise required for cell type annotation. Additionally, we have developed an R software package GPTCelltype for GPT-4's automated cell type annotation.

Single-cell transcriptome analysis deciphers the CD74-mediated immune evasion and tumour growth in lung squamous cell carcinoma with chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) contributes to the incidence and prognosis of lung cancer. The presence of COPD significantly increases the risk of lung squamous cell carcinoma (LSCC). COPD may promote an immunosuppressive microenvironment in LSCC by regulating the expression of immune-inhibitory factors in T cells, although the mechanisms remain unclear. In this study, we aimed to decipher the tumour microenvironment signature for LSCC with COPD at a single-cell level.

METHODS

We performed single-cell RNA sequencing on tumour tissues from LSCC with or without COPD, then investigated the features of the immune and tumour cells. We employed multiple techniques, including multispectral imaging, flow cytometry, tissue microarray analysis, survival analysis, co-culture systems and in vitro and in vivo treatment experiments, to validate the findings obtained from single-cell analyses.

RESULTS

LSCC with COPD showed increased proportions of tumour-associated macrophages (TAMs) and higher levels of CD8+ T cell exhaustion molecules, which contributed to an immunosuppressive microenvironment. Further analysis revealed a critical cluster of CD74+ tumour cells that expressed both epithelial and immune cell signatures, exhibited a stronger capacity for tumorigenesis and predicted worse overall survival. Notably, migration inhibitory factor (MIF) secreted by TAMs from LSCC with COPD may promote the activation of CD74. MIF-CD74 may interact with CD8+ T cells and impair their anti-tumour activity by regulating the PI3K-STAT3-programmed cell death-1 ligand 1 signalling pathway, facilitating tumour proliferation and immune evasion.

CONCLUSIONS

Our comprehensive picture of the tumour ecosystem in LSCC with COPD provides deeper insights into relevant immune evasion mechanisms and potential targets for immunotherapy.

HIGHLIGHT

Our results demonstrated higher proportions of tumour-associated macrophages (TAMs) and higher levels of exhaustion molecules in CD8+ T cells in the microenvironment of LSCC with COPD. CD74+tumour cells were associated with poor disease prognosis. Migration inhibitory factor (MIF)-CD74 may interact with CD8+ T cells and impair their anti-tumour activity by regulating the PI3K-STAT3-PD-L1 signalling pathway, facilitating immune evasion.

scGPT: toward building a foundation model for single-cell multi-omics using generative AI

Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically, the combination of large-scale diverse datasets and pretrained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between language and cellular biology (in which texts comprise words; similarly, cells are defined by genes), our study probes the applicability of foundation models to advance cellular biology and genetic research. Using burgeoning single-cell sequencing data, we have constructed a foundation model for single-cell biology, scGPT, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction and gene network inference.

The crosstalk between lung cancer and the bone marrow niche fuels emergency myelopoiesis

Modest response rates to immunotherapy observed in advanced lung cancer patients underscore the need to identify reliable biomarkers and targets, enhancing both treatment decision-making and efficacy. Factors such as PD-L1 expression, tumor mutation burden, and a 'hot' tumor microenvironment with heightened effector T cell infiltration have consistently been associated with positive responses. In contrast, the predictive role of the abundantly present tumor-infiltrating myeloid cell (TIMs) fraction remains somewhat uncertain, partly explained by their towering variety in terms of ontogeny, phenotype, location, and function. Nevertheless, numerous preclinical and clinical studies established a clear link between lung cancer progression and alterations in intra- and extramedullary hematopoiesis, leading to emergency myelopoiesis at the expense of megakaryocyte/erythroid and lymphoid differentiation. These observations affirm that a continuous crosstalk between solid cancers such as lung cancer and the bone marrow niche (BMN) must take place. However, the BMN, encompassing hematopoietic stem and progenitor cells, differentiated immune and stromal cells, remains inadequately explored in solid cancer patients. Subsequently, no clear consensus has been reached on the exact breadth of tumor installed hematopoiesis perturbing cues nor their predictive power for immunotherapy. As the current era of single-cell omics is reshaping our understanding of the hematopoietic process and the subcluster landscape of lung TIMs, we aim to present an updated overview of the hierarchical differentiation process of TIMs within the BMN of solid cancer bearing subjects. Our comprehensive overview underscores that lung cancer should be regarded as a systemic disease in which the cues governing the lung tumor-BMN crosstalk might bolster the definition of new biomarkers and druggable targets, potentially mitigating the high attrition rate of leading immunotherapies for NSCLC.

Transcriptomics Analysis Identifies the Decline in the Alveolar Type II Stem Cell Niche in Aged Human Lungs

Aging poses a global public health challenge, which is linked to the rise of age-related lung diseases. The precise understanding of the molecular and genetic changes in the aging lung that elevate the risk of acute and chronic lung diseases remains incomplete. Alveolar type II (AT2) cells are stem cells that maintain epithelial homeostasis and repair the lung after injury. AT2 progenitor function decreases with aging. The maintenance of AT2 function requires niche support from other cell types, but little has been done to characterize alveolar alterations with aging in the AT2 niche. To systematically profile the genetic changes associated with age, we present a single-cell transcriptional atlas comprising nearly half a million cells from the healthy lungs of human subjects spanning various ages, sexes, and smoking statuses. Most annotated cell lineages in aged lungs exhibit dysregulated genetic programs. Specifically, the aged AT2 cells demonstrate loss of epithelial identities, heightened inflammaging characterized by increased expression of AP-1 (Activator Protein-1) transcription factor and chemokine genes, and significantly increased cellular senescence. Furthermore, the aged mesenchymal cells display a remarkable decrease in collagen and elastin transcription and a loss of support to epithelial cell stemness. The decline of the AT2 niche is further exacerbated by a dysregulated genetic program in macrophages and dysregulated communications between AT2 and macrophages in aged human lungs. These findings highlight the dysregulations observed in both AT2 stem cells and their supportive niche cells, potentially contributing to the increased susceptibility of aged populations to lung diseases.

The prognostic impact of pathogenic stromal cell-associated genes in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) stands as the most prevalent subtype among lung cancers. Interactions between stromal and cancer cells influence tumor growth, invasion, and metastasis. However, the regulatory mechanisms of stromal cells in the lung adenocarcinoma tumor microenvironment remain unclear. This study seeks to elucidate the regulatory connections among critical pathogenic genes and their associated expression variations within distinct stromal cell subtypes.

METHOD

Analysis and investigation were conducted on a total of 114,019 single-cell RNA data and 346 The Cancer Genome Atlas (TCGA) LUAD-related samples using bioinformatics and statistical algorithms. Differential gene expression analysis was performed for tumor samples and controls, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Differential genes between stromal cells and other cell clusters were identified and intersected with the differential genes from TCGA. We employed a combination of LASSO regression and multivariable Cox regression to identify the ultimate set of pathogenic gene. Survival models were trained to predict the relationship between patient survival and these pathogenic genes. Analysis of transcription factor (TF) cell specificity and pseudotime trajectories within stromal cell subpopulations revealed that vascular endothelial cells (ECs) and matrix cancer-associated fibroblasts (CAFs) are key in regulation of the prognosis-associated genes CAV2, COL1A1, TIMP1, ETS2, AKAP12, ID1 and COL1A2.

RESULTS

Seven pathogenic genes associated with LUAD in stromal cells were identified and used to develop a survival model. High expression of these genes is linked to a greater risk of poor survival. Stromal cells were categorized into eight subtypes and one unannotated cluster. Mesothelial cells, vascular endothelial cells (ECs), and matrix cancer-associated fibroblasts (CAFs) showed cell-specific regulation of the pathogenic genes.

CONCLUSIONS

The seven disease-causing genes in vascular ECs and matrix CAFs can be used to detect the survival status of LUAD patients, providing new directions for future targeted drug design.

Single-cell atlas of the human brain vasculature across development, adulthood and disease

A broad range of brain pathologies critically relies on the vasculature, and cerebrovascular disease is a leading cause of death worldwide. However, the cellular and molecular architecture of the human brain vasculature remains incompletely understood1. Here we performed single-cell RNA sequencing analysis of 606,380 freshly isolated endothelial cells, perivascular cells and other tissue-derived cells from 117 samples, from 68 human fetuses and adult patients to construct a molecular atlas of the developing fetal, adult control and diseased human brain vasculature. We identify extensive molecular heterogeneity of the vasculature of healthy fetal and adult human brains and across five vascular-dependent central nervous system (CNS) pathologies, including brain tumours and brain vascular malformations. We identify alteration of arteriovenous differentiation and reactivated fetal as well as conserved dysregulated genes and pathways in the diseased vasculature. Pathological endothelial cells display a loss of CNS-specific properties and reveal an upregulation of MHC class II molecules, indicating atypical features of CNS endothelial cells. Cell-cell interaction analyses predict substantial endothelial-to-perivascular cell ligand-receptor cross-talk, including immune-related and angiogenic pathways, thereby revealing a central role for the endothelium within brain neurovascular unit signalling networks. Our single-cell brain atlas provides insights into the molecular architecture and heterogeneity of the developing, adult/control and diseased human brain vasculature and serves as a powerful reference for future studies.

Monocytes-to-lymphocytes ratio increases the prognostic value of circulating tumor cells in non-small cell lung cancer: a prospective study

Background

Circulating tumor cells (CTCs) has shown important prognostic value in non-small cell lung cancer (NSCLC). However, the present low sensitivity of CTC capture technology restricts their clinical application. This study aims to explore the feasibility of combining the peripheral blood cell (PBC)-derived inflammation-based score with CTCs to increase the prognostic value of CTCs in NSCLC.

Methods

Sixty volunteers diagnosed with NSCLC were recruited. CTC count and six inflammation-based scores were examined and the association with progression-free survival (PFS) and overall survival (OS) was explored. The changes in the CTC counts before and after the immunotherapy were observed.

Results

Multivariate analysis showed that CTCs >7 [hazard ratio (HR) =9.07; 95% confidence interval (CI): 3.68-22.37, P<0.001] and monocytes-to-lymphocytes ratio (MLR) > 0.2 (HR =3.07; 95% CI: 1.21-7.84; P=0.01) were associated with shorter OS and PFS in patients with NSCLC. Patients with CTCs >7 and MLR >0.2 had 12.30 times increased risk of death (P<0.001) and 6.10 times increased risk of disease progression (P=0.002) compared with those with CTCs ≤7 and MLR ≤0.2. Decreased CTC counts after immunotherapy were closely related to disease control (r=0.535, P=0.01).

Conclusions

CTCs and MLR are both independent risk factors for prognosis in patients with NSCLC. The combination of CTCs with MLR significantly increased the prognostic value of CTCs, which would contribute to stratification of NSCLC patients and providing precise treatment. Dynamic monitoring of CTCs efficiently shows the immunotherapy response in NSCLC.

Hypoxia drives HIF2-dependent reversible macrophage cell cycle entry

Low-oxygen conditions (hypoxia) have been associated primarily with cell-cycle arrest in dividing cells. Macrophages are typically quiescent in G0 but can proliferate in response to tissue signals. Here we show that hypoxia (1% oxygen tension) results in reversible entry into the cell cycle in macrophages. Cell cycle progression is largely limited to G0-G1/S phase transition with little progression to G2/M. This cell cycle transitioning is triggered by an HIF2α-directed transcriptional program. The response is accompanied by increased expression of cell-cycle-associated proteins, including CDK1, which is known to phosphorylate SAMHD1 at T592 and thereby regulate antiviral activity. Prolyl hydroxylase (PHD) inhibitors are able to recapitulate HIF2α-dependent cell cycle entry in macrophages. Finally, tumor-associated macrophages (TAMs) in lung cancers exhibit transcriptomic profiles representing responses to low oxygen and cell cycle progression at the single-cell level. These findings have implications for inflammation and tumor progression/metastasis where low-oxygen environments are common.

Accurate estimation of pathway activity in single cells for clustering and differential analysis

Inferring which and how biological pathways and gene sets change is a key question in many studies that utilize single-cell RNA sequencing. Typically, these questions are addressed by quantifying the enrichment of known gene sets in lists of genes derived from global analysis. Here we offer SiPSiC, a new method to infer pathway activity in every single cell. This allows more sensitive differential analysis and utilization of pathway scores to cluster cells and compute UMAP or other similar projections. We apply our method to COVID-19, lung adenocarcinoma and glioma data sets, and demonstrate its utility. SiPSiC analysis results are consistent with findings reported in previous studies in many cases, but SiPSiC also reveals the differential activity of novel pathways, enabling us to suggest new mechanisms underlying the pathophysiology of these diseases and demonstrating SiPSiC's high accuracy and sensitivity in detecting biological function and traits. In addition, we demonstrate how it can be used to better classify cells based on activity of biological pathways instead of single genes and its ability to overcome patient-specific artifacts.

Pan-cancer mapping of single CD8+ T cell profiles reveals a TCF1:CXCR6 axis regulating CD28 co-stimulation and anti-tumor immunity

CD8+ T cells must persist and function in diverse tumor microenvironments to exert their effects. Thus, understanding common underlying expression programs could better inform the next generation of immunotherapies. We apply a generalizable matrix factorization algorithm that recovers both shared and context-specific expression programs from diverse datasets to a single-cell RNA sequencing (scRNA-seq) compendium of 33,161 CD8+ T cells from 132 patients with seven human cancers. Our meta-single-cell analyses uncover a pan-cancer T cell dysfunction program that predicts clinical non-response to checkpoint blockade in melanoma and highlights CXCR6 as a pan-cancer marker of chronically activated T cells. Cxcr6 is trans-activated by AP-1 and repressed by TCF1. Using mouse models, we show that Cxcr6 deletion in CD8+ T cells increases apoptosis of PD1+TIM3+ cells, dampens CD28 signaling, and compromises tumor growth control. Our study uncovers a TCF1:CXCR6 axis that counterbalances PD1-mediated suppression of CD8+ cell responses and is essential for effective anti-tumor immunity.

Using a pan-cancer atlas to investigate tumour associated macrophages as regulators of immunotherapy response

The paradigm for macrophage characterization has evolved from the simple M1/M2 dichotomy to a more complex model that encompasses the broad spectrum of macrophage phenotypic diversity, due to differences in ontogeny and/or local stimuli. We currently lack an in-depth pan-cancer single cell RNA-seq (scRNAseq) atlas of tumour-associated macrophages (TAMs) that fully captures this complexity. In addition, an increased understanding of macrophage diversity could help to explain the variable responses of cancer patients to immunotherapy. Our atlas includes well established macrophage subsets as well as a number of additional ones. We associate macrophage composition with tumour phenotype and show macrophage subsets can vary between primary and metastatic tumours growing in sites like the liver. We also examine macrophage-T cell functional cross talk and identify two subsets of TAMs associated with T cell activation. Analysis of TAM signatures in a large cohort of immune checkpoint inhibitor-treated patients (CPI1000 + ) identify multiple TAM subsets associated with response, including the presence of a subset of TAMs that upregulate collagen-related genes. Finally, we demonstrate the utility of our data as a resource and reference atlas for mapping of novel macrophage datasets using projection. Overall, these advances represent an important step in both macrophage classification and overcoming resistance to immunotherapies in cancer.

Mapping the landscape and exploring trends in macrophage-related research within non-small cell lung cancer: a comprehensive bibliometric analysis

Background

Macrophages play a pivotal role in the research landscape of non-small cell lung cancer (NSCLC), contributing significantly to understanding tumor progression, treatment resistance, and immunotherapy efficacy. In this study, we utilized bibliometric techniques to analyze shifts in research hotspots and trends within the field, while also forecasting future research directions. These insights aim to offer guidance for both clinical therapeutic interventions and foundational scientific inquiries.

Methods

All publications were released between 1993 and 2023 and focus on research pertaining to macrophages in the field of NSCLC. The articles were identified from the Web of Science Core Collection and analyzed using VOSviewer 1.6.19, CiteSpace 6.2.R2, and Scimago Graphica 1.0.35.

Result

A total of 361 articles authored by 3,072 researchers from 48 countries were included in the analysis. TAMs have gained increasing attention for their role in NSCLC development and as potential therapeutic targets. Modulating TAM behavior may offer avenues to suppress tumor growth and drug resistance, improving patient outcomes. International collaboration, particularly between China and the United States, accelerates progress in NSCLC research, benefiting patients worldwide. The research hotspot revolves around understanding the role of macrophages in immunotherapy, focusing on their contribution to tumor progression, therapeutic resistance, and potential as therapeutic targets in NSCLC.

Conclusions

The therapeutic significance of macrophages in the field of NSCLC is gaining increasing attention and recognition, highlighting their potential as key players in the development of novel treatment strategies. Future research will focus on understanding TAM molecular mechanisms, interactions with immune cells, and exploring novel therapies, with the aim of improving NSCLC treatment outcomes.

NTSR1 promotes epithelial-mesenchymal transition and metastasis in lung adenocarcinoma through the Wnt/β-catenin pathway

BACKGROUND

Lung adenocarcinoma (LUAD) patients are implicated in poor prognoses and increased mortality rates. Metastasis, as a leading cause of LUAD-related deaths, requires further investigation. Highly metastatic cancer cells often exhibit extensive characteristics of epithelial-mesenchymal transition (EMT). This study attempted to identify novel targets associated with LUAD metastasis and validate their specific molecular mechanisms.

METHODS

Bioinformatics was conducted to determine NTSR1 expression in LUAD and the enriched pathways. Immunohistochemical analysis was used to assess NTSR1 expression in LUAD tissue. qRT-PCR examined expressions of NTSR1 and Wnt/β-Catenin pathway-related genes in LUAD cells. Transwell assayed cell migration and invasion. Cell adhesion experiments were conducted to evaluate cell adhesion capacity. Western blot analysis was employed to examine expression of EMT, Wnt/β-Catenin pathway, and cell adhesion markers.

RESULTS

NTSR1 was upregulated in LUAD tissues and cells, and enriched in EMT pathway. Knockdown of NTSR1 reduced migration, invasion, and adhesion abilities in LUAD cells, and inhibited EMT progression and Wnt/β-Catenin pathway. Rescue experiments demonstrated that β-Catenin activator SKL2001 reversed repressive influence of NTSR1 knockdown on LUAD cell malignant phenotypes and EMT progression.

CONCLUSION

The data obtained in this study suggested that NTSR1 stimulated EMT and metastasis in LUAD via Wnt/β-Catenin pathway. This finding may provide options for overcoming LUAD metastasis.

Brain Metastasis from EGFR-Mutated Non-Small Cell Lung Cancer: Secretion of IL11 from Astrocytes Up-Regulates PDL1 and Promotes Immune Escape

Patients who have non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutations are more prone to brain metastasis (BM) and poor prognosis. Previous studies showed that the tumor microenvironment of BM in these patients is immunosuppressed, as indicated by reduced T-cell abundance and activity, although the mechanism of this immunosuppression requires further study. This study shows that reactive astrocytes play a critical role in promoting the immune escape of BM from EGFR-mutated NSCLC by increasing the apoptosis of CD8+ T lymphocytes. The increased secretion of interleukin 11(IL11) by astrocytes promotes the expression of PDL1 in BM, and this is responsible for the increased apoptosis of T lymphocytes. IL11 functions as a ligand of EGFR, and this binding activates EGFR and downstream signaling to increase the expression of PDL1, culminating in the immune escape of tumor cells. IL11 also promotes immune escape by binding to its intrinsic receptor (IL11Rα/glycoprotein 130 [gp130]). Additional in vivo studies show that the targeted inhibition of gp130 and EGFR suppresses the growth of BM and prolongs the survival time of mice. These results suggest a novel therapeutic strategy for treatment of NSCLC patients with EGFR mutations.

PD-L1 promotes oncolytic virus infection via a metabolic shift that inhibits the type I IFN pathway

While conventional wisdom initially postulated that PD-L1 serves as the inert ligand for PD-1, an emerging body of literature suggests that PD-L1 has cell-intrinsic functions in immune and cancer cells. In line with these studies, here we show that engagement of PD-L1 via cellular ligands or agonistic antibodies, including those used in the clinic, potently inhibits the type I interferon pathway in cancer cells. Hampered type I interferon responses in PD-L1-expressing cancer cells resulted in enhanced efficacy of oncolytic viruses in vitro and in vivo. Consistently, PD-L1 expression marked tumor explants from cancer patients that were best infected by oncolytic viruses. Mechanistically, PD-L1 promoted a metabolic shift characterized by enhanced glycolysis rate that resulted in increased lactate production. In turn, lactate inhibited type I IFN responses. In addition to adding mechanistic insight into PD-L1 intrinsic function, our results will also help guide the numerous ongoing efforts to combine PD-L1 antibodies with oncolytic virotherapy in clinical trials.

Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods

BACKGROUND

Computational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions.

RESULTS

In our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers.

CONCLUSIONS

Our heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly package https://github.com/humengying0907/deconvBenchmarking and https://doi.org/10.5281/zenodo.8206516 , enabling further developments in deconvolution methods.

From monocyte-derived macrophages to resident macrophages-how metabolism leads their way in cancer

Macrophages are innate immune cells that play key roles during both homeostasis and disease. Depending on the microenvironmental cues sensed in different tissues, macrophages are known to acquire specific phenotypes and exhibit unique features that, ultimately, orchestrate tissue homeostasis, defense, and repair. Within the tumor microenvironment, macrophages are referred to as tumor-associated macrophages (TAMs) and constitute a heterogeneous population. Like their tissue resident counterpart, TAMs are plastic and can switch function and phenotype according to the niche-derived stimuli sensed. While changes in TAM phenotype are known to be accompanied by adaptive alterations in their cell metabolism, it is reported that metabolic reprogramming of macrophages can dictate their activation state and function. In line with these observations, recent research efforts have been focused on defining the metabolic traits of TAM subsets in different tumor malignancies and understanding their role in cancer progression and metastasis formation. This knowledge will pave the way to novel therapeutic strategies tailored to cancer subtype-specific metabolic landscapes. This review outlines the metabolic characteristics of distinct TAM subsets and their implications in tumorigenesis across multiple cancer types.

A Pathologically Friendly Strategy for Determining the Organ-specific Spatial Tumor Microenvironment Topology in Lung Adenocarcinoma Through the Integration of snRandom-seq and Imaging Mass Cytometry

Heterogeneous organ-specific responses to immunotherapy exist in lung cancer. Dissecting tumor microenvironment (TME) can provide new insights into the mechanisms of divergent responses, the process of which remains poor, partly due to the challenges associated with single-cell profiling using formalin-fixed paraffin-embedded (FFPE) materials. In this study, single-cell nuclei RNA sequencing and imaging mass cytometry (IMC) are used to dissect organ-specific cellular and spatial TME based on FFPE samples from paired primary lung adenocarcinoma (LUAD) and metastases. Single-cell analyses of 84 294 cells from sequencing and 250 600 cells from IMC reveal divergent organ-specific immune niches. For sites of LUAD responding well to immunotherapy, including primary LUAD and adrenal gland metastases, a significant enrichment of B, plasma, and T cells is detected. Spatially resolved maps reveal cellular neighborhoods recapitulating functional units of the tumor ecosystem and the spatial proximity of B and CD4+ T cells at immunogenic sites. Various organ-specific densities of tertiary lymphoid structures are observed. Immunosuppressive sites, including brain and liver metastases, are deposited with collagen I, and T cells at these sites highly express TIM-3. This study originally deciphers the single-cell landscape of the organ-specific TME at both cellular and spatial levels for LUAD, indicating the necessity for organ-specific treatment approaches.

Deciphering lung adenocarcinoma prognosis and immunotherapy response through an AI-driven stemness-related gene signature

Lung adenocarcinoma (LUAD) is a leading cause of cancer-related deaths, and improving prognostic accuracy is vital for personalised treatment approaches, especially in the context of immunotherapy. In this study, we constructed an artificial intelligence (AI)-driven stemness-related gene signature (SRS) that deciphered LUAD prognosis and immunotherapy response. CytoTRACE analysis of single-cell RNA sequencing data identified genes associated with stemness in LUAD epithelial cells. An AI network integrating traditional regression, machine learning, and deep learning algorithms constructed the SRS based on genes associated with stemness. Subsequently, we conducted a comprehensive exploration of the connection between SRS and both intrinsic and extrinsic immune environments using multi-omics data. Experimental validation through siRNA knockdown in LUAD cell lines, followed by assessments of proliferation, migration, and invasion, confirmed the functional role of CKS1B, a top SRS gene. The SRS demonstrated high precision in predicting LUAD prognosis and likelihood of benefiting from immunotherapy. High-risk groups classified by the SRS exhibited decreased immunogenicity and reduced immune cell infiltration, indicating challenges for immunotherapy. Conversely, in vitro experiments revealed CKS1B knockdown significantly impaired aggressive cancer phenotypes like proliferation, migration, and invasion of LUAD cells, highlighting its pivotal role. These results underscore a close association between stemness and tumour immunity, offering predictive insights into the immune landscape and immunotherapy responses in LUAD. The newly established SRS holds promise as a valuable tool for selecting LUAD populations likely to benefit from future clinical stratification efforts.

Exploring the prognostic analysis of autophagy and tumor microenvironment based on monocyte cells in lung cancer

A deep understanding of the biological mechanisms of lung cancer offers more precise treatment options for patients. In our study, we integrated data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) to investigate lung adenocarcinoma. Analyzing 538 lung cancer samples and 31 normal samples, we focused on 3076 autophagy-related genes. Using Seurat, dplyr, tidyverse, and ggplot2, we conducted single-cell data analysis, assessing the quality and performing Principal Component Analysis (PCA) and t-SNE analyses. Differential analysis of TCGA data using the "Limma" package, followed by immune infiltration analysis using the CIBERSORT algorithm, led us to identify seven key genes. These genes underwent further scrutiny through consensus clustering and gene set variation analysis (GSVA). We developed a prognostic model using Lasso Cox regression and multivariable Cox analysis, which was then validated with a nomogram, predicting survival rates for lung adenocarcinoma. The model's accuracy and universality were corroborated by ROC curves. Additionally, we explored the relationship between immune checkpoint genes and immune cell infiltration and identified two key genes, HLA-DQB1 and OLR1. This highlighted their potential as therapeutic targets. Our comprehensive approach sheds light on the molecular landscape of lung adenocarcinoma and offers insights into potential treatment strategies, emphasizing the importance of integrating single-cell and genomic data in cancer research.

Decoupling the correlation between cytotoxic and exhausted T lymphocyte states enhances melanoma immunotherapy response prediction

Cytotoxic T lymphocyte (CTL) and terminal exhausted T lymphocyte (ETL) activities crucially influence immune checkpoint inhibitor (ICI) response. Despite this, the efficacy of ETL and CTL transcriptomic signatures for response prediction remains limited. Investigating this across the TCGA and publicly available single-cell cohorts, we find a strong positive correlation between ETL and CTL expression signatures in most cancers. We hence posited that their limited predictability arises due to their mutually canceling effects on ICI response. Thus, we developed DETACH, a computational method to identify a gene set whose expression pinpoints to a subset of melanoma patients where the CTL and ETL correlation is low. DETACH enhances CTL's prediction accuracy, outperforming existing signatures. DETACH signature genes activity also demonstrates a positive correlation with lymphocyte infiltration and the prevalence of reactive T cells in the tumor microenvironment (TME), advancing our understanding of the CTL cell state within the TME.

Challenges and advances in the management of inflammation in atherosclerosis

INTRODUCTION

Atherosclerosis, traditionally considered a lipid-related disease, is now understood as a chronic inflammatory condition with significant global health implications.

OBJECTIVES

This review aims to delve into the complex interactions among immune cells, cytokines, and the inflammatory cascade in atherosclerosis, shedding light on how these elements influence both the initiation and progression of the disease.

METHODS

This review draws on recent clinical research to elucidate the roles of key immune cells, macrophages, T cells, endothelial cells, and clonal hematopoiesis in atherosclerosis development. It focuses on how these cells and process contribute to disease initiation and progression, particularly through inflammation-driven processes that lead to plaque formation and stabilization. Macrophages ingest oxidized low-density lipoprotein (oxLDL), which partially converts to high-density lipoprotein (HDL) or accumulates as lipid droplets, forming foam cells crucial for plaque stability. Additionally, macrophages exhibit diverse phenotypes within plaques, with pro-inflammatory types predominating and others specializing in debris clearance at rupture sites. The involvement of CD4+ T and CD8+ T cells in these processes promotes inflammatory macrophage states, suppresses vascular smooth muscle cell proliferation, and enhances plaque instability.

RESULTS

The nuanced roles of macrophages, T cells, and the related immune cells within the atherosclerotic microenvironment are explored, revealing insights into the cellular and molecular pathways that fuel inflammation. This review also addresses recent advancements in imaging and biomarker technology that enhance our understanding of disease progression. Moreover, it points out the limitations of current treatment and highlights the potential of emerging anti-inflammatory strategies, including clinical trials for agents such as p38MAPK, tumor necrosis factor α (TNF-α), and IL-1β, their preliminary outcomes, and the promising effects of canakinumab, colchicine, and IL-6R antagonists.

CONCLUSION

This review explores cutting-edge anti-inflammatory interventions, their potential efficacy in preventing and alleviating atherosclerosis, and the role of nanotechnology in delivering drugs more effectively and safely.

[Advancements in Single-cell RNA Sequencing Technology in the Study of the Tumor Microenvironment in Lung Cancer]

The immune microenvironment plays a key role in the development and progression of tumors. In recent years, with the rapid advancement of high-throughput sequencing technologies, researchers have gained a deeper understanding of the composition and function of immune cells in the tumor microenvironment. However, traditional bulk sequencing technologies are limited in resolving heterogeneity at the single-cell level, constraining a comprehensive understanding of the complexity of the tumor microenvironment. The advent of single-cell RNA sequencing technology has brought new opportunities to uncover the heterogeneity of the immune microenvironment in lung cancer. Currently, T-cell-centered immunotherapy in clinical settings is prone to side effects affecting prognosis, such as immunogenic drug resistance or immune-related pneumonia, with the key factor being changes in the interactions between immune cells and tumor cells in the tumor microenvironment. Single-cell RNA sequencing technology can reveal the origins and functions of different subgroups within the tumor microenvironment from perspectives such as intercellular interactions and pseudotime analysis, thereby discovering new cell subgroups or novel biomarkers, providing new avenues for uncovering resistance to immunotherapy and monitoring therapeutic efficacy. This review comprehensively discusses the newest research techniques and advancements in single-cell RNA sequencing technology for unveiling the heterogeneity of the tumor microenvironment after lung cancer immunotherapy, offering insights for enhancing the precision and personalization of immunotherapy.
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The Advancement and Application of the Single-Cell Transcriptome in Biological and Medical Research

Single-cell RNA sequencing technology (scRNA-seq) has been steadily developing since its inception in 2009. Unlike bulk RNA-seq, scRNA-seq identifies the heterogeneity of tissue cells and reveals gene expression changes in individual cells at the microscopic level. Here, we review the development of scRNA-seq, which has gone through iterations of reverse transcription, in vitro transcription, smart-seq, drop-seq, 10 × Genomics, and spatial single-cell transcriptome technologies. The technology of 10 × Genomics has been widely applied in medicine and biology, producing rich research results. Furthermore, this review presents a summary of the analytical process for single-cell transcriptome data and its integration with other omics analyses, including genomes, epigenomes, proteomes, and metabolomics. The single-cell transcriptome has a wide range of applications in biology and medicine. This review analyzes the applications of scRNA-seq in cancer, stem cell research, developmental biology, microbiology, and other fields. In essence, scRNA-seq provides a means of elucidating gene expression patterns in single cells, thereby offering a valuable tool for scientific research. Nevertheless, the current single-cell transcriptome technology is still imperfect, and this review identifies its shortcomings and anticipates future developments. The objective of this review is to facilitate a deeper comprehension of scRNA-seq technology and its applications in biological and medical research, as well as to identify avenues for its future development in alignment with practical needs.

TMPRSS2 is a tumor suppressor and its downregulation promotes antitumor immunity and immunotherapy response in lung adenocarcinoma

BACKGROUND

TMPRSS2, a key molecule for SARS-CoV-2 invading human host cells, has an association with cancer. However, its association with lung cancer remains insufficiently unexplored.

METHODS

In five bulk transcriptomics datasets, one single-cell RNA sequencing (scRNA-seq) dataset and one proteomics dataset for lung adenocarcinoma (LUAD), we explored associations between TMPRSS2 expression and immune signatures, tumor progression phenotypes, genomic features, and clinical prognosis in LUAD by the bioinformatics approach. Furthermore, we performed experimental validation of the bioinformatics findings.

RESULTS

TMPRSS2 expression levels correlated negatively with the enrichment levels of both immune-stimulatory and immune-inhibitory signatures, while they correlated positively with the ratios of immune-stimulatory/immune-inhibitory signatures. It indicated that TMPRSS2 levels had a stronger negative correlation with immune-inhibitory than with immune-stimulatory signatures. TMPRSS2 downregulation correlated with increased proliferation, stemness, genomic instability, tumor progression, and worse survival in LUAD. We further validated that TMPRSS2 was downregulated with tumor progression in the LUAD cohort we collected from Jiangsu Cancer Hospital, China. In vitro and in vivo experiments verified the association of TMPRSS2 deficiency with increased tumor cell proliferation and invasion and antitumor immunity in LUAD. Moreover, in vivo experiments demonstrated that TMPRSS2-knockdown tumors were more sensitive to BMS-1, an inhibitor of PD-1/PD-L1.

CONCLUSIONS

TMPRSS2 is a tumor suppressor, while its downregulation is a positive biomarker of immunotherapy in LUAD. Our data provide a potential link between lung cancer and pneumonia caused by SARS-CoV-2 infection.

The temporal progression of lung immune remodeling during breast cancer metastasis

Tumor metastasis requires systemic remodeling of distant organ microenvironments that impacts immune cell phenotypes, population structure, and intercellular communication. However, our understanding of immune phenotypic dynamics in the metastatic niche remains incomplete. Here, we longitudinally assayed lung immune transcriptional profiles in the polyomavirus middle T antigen (PyMT) and 4T1 metastatic breast cancer models from primary tumorigenesis, through pre-metastatic niche formation, to the final stages of metastatic outgrowth at single-cell resolution. Computational analyses of these data revealed a TLR-NFκB inflammatory program enacted by both peripherally derived and tissue-resident myeloid cells that correlated with pre-metastatic niche formation and mirrored CD14+ "activated" myeloid cells in the primary tumor. Moreover, we observed that primary tumor and metastatic niche natural killer (NK) cells are differentially regulated in mice and human patient samples, with the metastatic niche featuring elevated cytotoxic NK cell proportions. Finally, we identified cell-type-specific dynamic regulation of IGF1 and CCL6 signaling during metastatic progression that represents anti-metastatic immunotherapy candidate pathways.

A developmental constraint model of cancer cell states and tumor heterogeneity

Cancer is a disease that stems from a fundamental liability inherent to multicellular life forms in which an individual cell is capable of reneging on the interests of the collective organism. Although cancer is commonly described as an evolutionary process, a less appreciated aspect of tumorigenesis may be the constraints imposed by the organism's developmental programs. Recent work from single-cell transcriptomic analyses across a range of cancer types has revealed the recurrence, plasticity, and co-option of distinct cellular states among cancer cell populations. Here, we note that across diverse cancer types, the observed cell states are proximate within the developmental hierarchy of the cell of origin. We thus posit a model by which cancer cell states are directly constrained by the organism's "developmental map." According to this model, a population of cancer cells traverses the developmental map, thereby generating a heterogeneous set of states whose interactions underpin emergent tumor behavior.

SIRPG expression positively associates with an inflamed tumor microenvironment and response to PD-1 blockade

BACKGROUND

This study aimed to investigate the relationship between signal regulatory protein gamma (SIRPG) and tumor immune microenvironment phenotypes or T cell mediated-adaptive antitumor immunity, and its predictive value for response to PD-1 blockade in cancers.

METHODS

Pan-cancer analysis of SIRPG expression and immune deconvolution was performed using transcriptomic data across 33 tumor types. Transcriptomic and clinical data from 157 patients with non-small-cell lung cancer (NSCLC) and melanoma received PD-1 blockade were analyzed. Expression characteristics of SIRPG were investigated using single-cell RNA sequencing (scRNA-seq) data of 103,599 cells. The effect of SIRPG expression was evaluated via SIRPG knockdown or overexpression in Jurkat T cells.

RESULTS

The results showed that most cancers with high SIRPG expression had significantly higher abundance of T cells, B cells, NK cells, M1 macrophages and cytotoxic lymphocytes and increased expression level of immunomodulatory factors regulating immune cell recruitment, antigen presentation, T cell activation and cytotoxicity, but markedly lower abundance of neutrophils, M2 macrophages, and myeloid-derived suppressor cells. High SIRPG expression was associated with favorable response to PD-1 blockade in both NSCLC and melanoma. scRNA-seq data suggested SIRPG was mainly expressed in CD8+ exhausted T and CD4+ regulatory T cells, and positively associated with immune checkpoint expression including PDCD1 and CTLA4. In vitro test showed SIRPG expression in T cells could facilitate expression of PDCD1 and CTLA4.

CONCLUSION

High SIRPG expression is associated with an inflamed immune phenotype in cancers and favorable response to PD-1 blockade, suggesting it would be a promising predictive biomarker for PD-1 blockade and novel immunotherapeutic target.

Biomaterials-mediated radiation-induced diseases treatment and radiation protection

In recent years, the intersection of the academic and medical domains has increasingly spotlighted the utilization of biomaterials in radioactive disease treatment and radiation protection. Biomaterials, distinguished from conventional molecular pharmaceuticals, offer a suite of advantages in addressing radiological conditions. These include their superior biological activity, chemical stability, exceptional histocompatibility, and targeted delivery capabilities. This review comprehensively delineates the therapeutic mechanisms employed by various biomaterials in treating radiological afflictions impacting the skin, lungs, gastrointestinal tract, and hematopoietic systems. Significantly, these nanomaterials function not only as efficient drug delivery vehicles but also as protective agents against radiation, mitigating its detrimental effects on the human body. Notably, the strategic amalgamation of specific biomaterials with particular pharmacological agents can lead to a synergistic therapeutic outcome, opening new avenues in the treatment of radiation- induced diseases. However, despite their broad potential applications, the biosafety and clinical efficacy of these biomaterials still require in-depth research and investigation. Ultimately, this review aims to not only bridge the current knowledge gaps in the application of biomaterials for radiation-induced diseases but also to inspire future innovations and research directions in this rapidly evolving field.

Contribution of crosstalk of mesothelial and tumoral epithelial cells in pleural metastasis of lung cancer

Background

Tumor metastasis commonly affects pleura in advanced lung cancer and results in malignant pleural effusion (MPE). MPE is related to poor prognosis, but without systematic investigation on different cell types and their crosstalk at single cell resolution.

Methods

We conducted single-cell RNA-sequencing (scRNA-seq) of lung cancer patients with pleural effusion. Next, our data were integrated with 5 datasets derived from individuals under normal, non-malignant disease and lung carcinomatous conditions. Mesothelial cells were re-clustered and their interactions with epithelial cells were comprehensively analyzed. Taking advantage of inferred ligand-receptor pairs, a prediction model of prognosis was constructed. The co-culture of mesothelial cells and malignant epithelial cells in vitro and RNA-seq was performed. Epidermal growth factor receptor (EGFR) antagonist cetuximab was utilized to prevent the lung cancer cells' invasiveness. Spatial distribution of cells in lung adenocarcinoma patients' samples were also analyzed to validate our findings.

Results

The most distinctive transcriptome profiles between tumor and control were revealed in mesothelial cells, which is the predominate cell type of pleura. Five subtypes were divided, including one predominately identified in MPE which was characterized by enriched cancer-related pathways (e.g., cell migration) along evolutionary trajectory from normal mesothelial cells. Cancer-associated mesothelial cells (CAMCs) exhibited varied interactions with different subtypes of malignant epithelial cells, and multiple ligands/receptors exhibited significant correlation with poor prognosis. Experimentally, mesothelial cells can increase the migration ability of lung cancer cells through co-culturing. EGFR was the only affected gene in cancer cells that exhibited interaction with mesothelial cells and was associated with poor prognosis. Using EGFR antagonist cetuximab prevented the lung cancer cells' increased invasiveness caused by mesothelial cells. Moreover, epithelial mitogen (EPGN)-EGFR interaction was supported through spatial distribution analysis, revealing the significant proximity between EPGN+ mesothelial cells and EGFR+ epithelial cells.

Conclusions

Our findings highlighted the important role of mesothelial cells and their interactions with cancer cells in pleural metastasis of lung cancer, providing potential targets for treatment.

PIPET: predicting relevant subpopulations in single-cell data using phenotypic information from bulk data

Single-cell RNA sequencing has revealed cellular heterogeneity in complex tissues, notably benefiting research on diseases such as cancer. However, the integration of single-cell data from small samples with extensive clinical features in bulk data remains underexplored. In this study, we introduce PIPET, an algorithmic method for predicting relevant subpopulations in single-cell data based on multivariate phenotypic information from bulk data. PIPET generates feature vectors for each phenotype from differentially expressed genes in bulk data and then identifies relevant cellular subpopulations by assessing the similarity between single-cell data and these vectors. Subsequently, phenotype-related cell states can be analyzed based on these subpopulations. In simulated datasets, PIPET showed robust performance in predicting multiclassification cellular subpopulations. Application of PIPET to lung adenocarcinoma single-cell RNA sequencing data revealed cellular subpopulations with poor survival and associations with TP53 mutations. Similarly, in breast cancer single-cell data, PIPET identified cellular subpopulations associated with the PAM50 clinical subtypes and triple-negative breast cancer subtypes. Overall, PIPET effectively identified relevant cellular subpopulations in single-cell data, guided by phenotypic information from bulk data. This approach comprehensively delineates the molecular characteristics of each cellular subpopulation, offering insights into disease-related subpopulations and guiding personalized treatment strategies.

Single-cell and spatial transcriptomics analysis of non-small cell lung cancer

Lung cancer is the second most frequently diagnosed cancer and the leading cause of cancer-related mortality worldwide. Tumour ecosystems feature diverse immune cell types. Myeloid cells, in particular, are prevalent and have a well-established role in promoting the disease. In our study, we profile approximately 900,000 cells from 25 treatment-naive patients with adenocarcinoma and squamous-cell carcinoma by single-cell and spatial transcriptomics. We note an inverse relationship between anti-inflammatory macrophages and NK cells/T cells, and with reduced NK cell cytotoxicity within the tumour. While we observe a similar cell type composition in both adenocarcinoma and squamous-cell carcinoma, we detect significant differences in the co-expression of various immune checkpoint inhibitors. Moreover, we reveal evidence of a transcriptional "reprogramming" of macrophages in tumours, shifting them towards cholesterol export and adopting a foetal-like transcriptional signature which promotes iron efflux. Our multi-omic resource offers a high-resolution molecular map of tumour-associated macrophages, enhancing our understanding of their role within the tumour microenvironment.

Characterizing efficient feature selection for single-cell expression analysis

Unsupervised feature selection is a critical step for efficient and accurate analysis of single-cell RNA-seq data. Previous benchmarks used two different criteria to compare feature selection methods: (i) proportion of ground-truth marker genes included in the selected features and (ii) accuracy of cell clustering using ground-truth cell types. Here, we systematically compare the performance of 11 feature selection methods for both criteria. We first demonstrate the discordance between these criteria and suggest using the latter. We then compare the distribution of selected genes in their means between feature selection methods. We show that lowly expressed genes exhibit seriously high coefficients of variation and are mostly excluded by high-performance methods. In particular, high-deviation- and high-expression-based methods outperform the widely used in Seurat package in clustering cells and data visualization. We further show they also enable a clear separation of the same cell type from different tissues as well as accurate estimation of cell trajectories.

GLDADec: marker-gene guided LDA modeling for bulk gene expression deconvolution

Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce guided LDA deconvolution (GLDADec), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to The Cancer Genome Atlas tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec.

HIF2A mediates lineage transition to aggressive phenotype of cancer-associated fibroblasts in lung cancer brain metastasis

Brain metastasis is the most devasting form of lung cancer. Recent studies highlight significant differences in the tumor microenvironment (TME) between lung cancer brain metastasis (LCBM) and primary lung cancer, which contribute significantly to tumor progression and drug resistance. Cancer-associated fibroblasts (CAFs) are the major component of pro-tumor TME with high plasticity. However, the lineage composition and function of CAFs in LCBM remain elusive. By reanalyzing single-cell RNA sequencing (scRNA-seq) data (GSE131907) from lung cancer patients with different stages of metastasis comprising primary lesions and brain metastasis, we found that CAFs undergo distinctive lineage transition during LCBM under a hypoxic situation, which is directly driven by hypoxia-induced HIF-2α activation. Transited CAFs enhance angiogenesis through VEGF pathways, trigger metabolic reprogramming, and promote the growth of tumor cells. Bulk RNA sequencing data was utilized as validation cohorts. Multiplex immunohistochemistry (mIHC) assay was performed on four paired samples of brain metastasis and their primary lung cancer counterparts to validate the findings. Our study revealed a novel mechanism of lung cancer brain metastasis featuring HIF-2α-induced lineage transition and functional alteration of CAFs, which offers potential therapeutic targets.

Inflammation-based lung adenocarcinoma molecular subtype identification and construction of an inflammation-related signature with bulk and single-cell RNA-seq data

The role of inflammation is increasingly understood to have a central influence on therapeutic outcomes and prognosis in lung adenocarcinoma (LUAD). However, the detailed molecular divisions involved in inflammatory responses are yet to be fully elucidated. Our study identified two main inflammation-oriented LUAD grades: the inflammation-low (INF-low) and the inflammation-high (INF-high) subtypes. Both presented with unique clinicopathological features, implications for prognosis, and distinctive tumor microenvironment profiles. Broadly, the INF-low grade, marked by its dominant immunosuppressive tumor microenvironment, was accompanied by less favorable prognostic outcomes and a heightened prevalence of oncogenic mutations. In contrast, the INF-high grade exhibited more optimistic clinical trajectories, underscored by its immune-active environment. In addition, our efforts led to the conceptualization and empirical validation of an inflammation-centric predictive model with considerable predictive potency. Our study paves the way for a refined inflammation-centric LUAD classification and fosters a deeper understanding of tumor microenvironment intricacies.

Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery

Recent advancements in modern science have provided robust tools for drug discovery. The rapid development of transcriptome sequencing technologies has given rise to single-cell transcriptomics and single-nucleus transcriptomics, increasing the accuracy of sequencing and accelerating the drug discovery process. With the evolution of single-cell transcriptomics, spatial transcriptomics (ST) technology has emerged as a derivative approach. Spatial transcriptomics has emerged as a hot topic in the field of omics research in recent years; it not only provides information on gene expression levels but also offers spatial information on gene expression. This technology has shown tremendous potential in research on disease understanding and drug discovery. In this article, we introduce the analytical strategies of spatial transcriptomics and review its applications in novel target discovery and drug mechanism unravelling. Moreover, we discuss the current challenges and issues in this research field that need to be addressed. In conclusion, spatial transcriptomics offers a new perspective for drug discovery.

SFXN1-mediated immune cell infiltration and tumorigenesis in lung adenocarcinoma: A potential therapeutic target

BACKGROUND

Sideroflexin 1 (SFXN1), a mitochondrial serine transporter implicated in one-carbon metabolism, is a prognostic biomarker in lung adenocarcinoma (LUAD). However, its role in LUAD progression remains elusive. This study aimed to investigate the functional significance of SFXN1 in LUAD and evaluate its potential as a therapeutic target.

METHODS

We analyzed SFXN1 expression and its diagnostic and prognostic value in LUAD using the Pan-cancer TCGA dataset. In vitro assays (CCK-8, cell cycle, EDU, wound-healing, and transwell) were employed to assess the role of SFXN1, complemented by in vivo experiments. RNA sequencing elucidated SFXN1-mediated cellular functions and potential mechanisms. Bulk RNA-seq and scRNA-seq data from TCGA and GEO were used to investigate the correlation between SFXN1 and the tumor immune microenvironment. RT-qPCR, Western blot, and IHC assays validated SFXN1 expression and its impact on the immune microenvironment in LUAD.

RESULTS

SFXN1 was upregulated in LUAD tissues and associated with poor prognosis. RNA-seq and scRNA-seq analyses revealed increased SFXN1 expression in tumor cells, accompanied by decreased infiltration of NK and cytotoxic T cells. SFXN1 knockdown significantly reduced cell proliferation and migration, and the inhibition of ERK phosphorylation and CCL20 expression may be the molecular mechanism involved. In vivo, targeting SFXN1 decreased Tregs infiltration and inhibited tumor growth.

CONCLUSIONS

Our findings suggest that SFXN1 may be a potential therapeutic target for LUAD treatment.

BiSpec Pairwise AI: guiding the selection of bispecific antibody target combinations with pairwise learning and GPT augmentation

PURPOSE

Bispecific antibodies (BsAbs), capable of targeting two antigens simultaneously, represent a significant advancement by employing dual mechanisms of action for tumor suppression. However, how to pair targets to develop effective and safe bispecific drugs is a major challenge for pharmaceutical companies.

METHODS

Using machine learning models, we refined the biological characteristics of currently approved or in clinical development BsAbs and analyzed hundreds of membrane proteins as bispecific targets to predict the likelihood of successful drug development for various target combinations. Moreover, to enhance the interpretability of prediction results in bispecific target combination, we combined machine learning models with Large Language Models (LLMs). Through a Retrieval-Augmented Generation (RAG) approach, we supplement each pair of bispecific targets' machine learning prediction with important features and rationales, generating interpretable analytical reports.

RESULTS

In this study, the XGBoost model with pairwise learning was employed to predict the druggability of BsAbs. By analyzing extensive data on BsAbs and designing features from perspectives such as target activity, safety, cell type specificity, pathway mechanism, and gene embedding representation, our model is able to predict target combinations of BsAbs with high market potential. Specifically, we integrated XGBoost with the GPT model to discuss the efficacy of each bispecific target pair, thereby aiding the decision-making for drug developers.

CONCLUSION

The novelty of this study lies in the integration of machine learning and GPT techniques to provide a novel framework for the design of BsAbs drugs. This holistic approach not only improves prediction accuracy, but also enhances the interpretability and innovativeness of drug design.

A blueprint for tumor-infiltrating B cells across human cancers

B lymphocytes are essential mediators of humoral immunity and play multiple roles in human cancer. To decode the functions of tumor-infiltrating B cells, we generated a B cell blueprint encompassing single-cell transcriptome, B cell-receptor repertoire, and chromatin accessibility data across 20 different cancer types (477 samples, 269 patients). B cells harbored extraordinary heterogeneity and comprised 15 subsets, which could be grouped into two independent developmental paths (extrafollicular versus germinal center). Tumor types grouped into the extrafollicular pathway were linked with worse clinical outcomes and resistance to immunotherapy. The dysfunctional extrafollicular program was associated with glutamine-derived metabolites through epigenetic-metabolic cross-talk, which promoted a T cell-driven immunosuppressive program. These data suggest an intratumor B cell balance between extrafollicular and germinal-center responses and suggest that humoral immunity could possibly be harnessed for B cell-targeting immunotherapy.

Single-Cell Analysis Identifies NOTCH3-Mediated Interactions between Stromal Cells That Promote Microenvironment Remodeling and Invasion in Lung Adenocarcinoma

Cancer immunotherapy has revolutionized the treatment of lung adenocarcinoma (LUAD); however, a significant proportion of patients do not respond. Recent transcriptomic studies to understand determinants of immunotherapy response have pinpointed stromal-mediated resistance mechanisms. To gain a better understanding of stromal biology at the cellular and molecular level in LUAD, we performed single-cell RNA sequencing of 256,379 cells, including 13,857 mesenchymal cells, from 9 treatment-naïve patients. Among the mesenchymal cell subsets, FAP+PDPN+ cancer-associated fibroblasts (CAF) and ACTA2+MCAM+ pericytes were enriched in tumors and differentiated from lung-resident fibroblasts. Imaging mass cytometry revealed that both subsets were topographically adjacent to the perivascular niche and had close spatial interactions with endothelial cells (EC). Modeling of ligand and receptor interactomes between mesenchymal and ECs identified that NOTCH signaling drives these cell-to-cell interactions in tumors, with pericytes and CAFs as the signal receivers and arterial and PLVAPhigh immature neovascular ECs as the signal senders. Either pharmacologically blocking NOTCH signaling or genetically depleting NOTCH3 levels in mesenchymal cells significantly reduced collagen production and suppressed cell invasion. Bulk RNA sequencing data demonstrated that NOTCH3 expression correlated with poor survival in stroma-rich patients and that a T cell-inflamed gene signature only predicted survival in patients with low NOTCH3. Collectively, this study provides valuable insights into the role of NOTCH3 in regulating tumor stroma biology, warranting further studies to elucidate the clinical implications of targeting NOTCH3 signaling.

SIGNIFICANCE

NOTCH3 signaling activates tumor-associated mesenchymal cells, increases collagen production, and augments cell invasion in lung adenocarcinoma, suggesting its critical role in remodeling tumor stroma.

Patient-derived tumor-like cell clusters for personalized chemo- and immunotherapies in non-small cell lung cancer

Many patient-derived tumor models have emerged recently. However, their potential to guide personalized drug selection remains unclear. Here, we report patient-derived tumor-like cell clusters (PTCs) for non-small cell lung cancer (NSCLC), capable of conducting 100-5,000 drug tests within 10 days. We have established 283 PTC models with an 81% success rate. PTCs contain primary tumor epithelium self-assembled with endogenous stromal and immune cells and show a high degree of similarity to the original tumors in phenotypic and genotypic features. Utilizing standardized culture and drug-response assessment protocols, PTC drug-testing assays reveal 89% overall consistency in prospectively predicting clinical outcomes, with 98.1% accuracy distinguishing complete/partial response from progressive disease. Notably, PTCs enable accurate prediction of clinical outcomes for patients undergoing anti-PD1 therapy by combining cell viability and IFN-γ value assessments. These findings suggest that PTCs could serve as a valuable preclinical model for personalized medicine and basic research in NSCLC.

ScRNA-seq of gastric cancer tissues reveals differences in the immune microenvironment of primary tumors and metastases

Gastric carcinoma (GC) is regarded as one of the deadliest cancer characterized by diversity and haste metastasis and suffers limited understanding of the spatial variation between primary and metastatic GC tumors. In this project, transcriptome analysis on 46 primary tumorous, adjacent non-tumorous, and metastatic GC tissues was performed. The results demonstrated that metastatic tumorous tissues had diminished CD8+ T cells compared to primary tumors, which is mechanistically attributed to being due to innate immunity differences represented by marked differences in macrophages between metastatic and primary tumors, particularly those expressing ApoE, where their abundance is linked to unfavorable prognoses. Examining variations in gene expression and interactions indicated possible strategies of immune evasion hindering the growth of CD8+ T cells in metastatic tumor tissues. More insights could be gained into the immune evasion mechanisms by portraying information about the GC ecosystem.

Insights and Opportunity Costs in Applying Spatial Biology to Study the Tumor Microenvironment

SUMMARY

The recent development of high-dimensional spatial omics tools has revealed the functional importance of the tumor microenvironment in driving tumor progression. Here, we discuss practical factors to consider when designing a spatial biology cohort and offer perspectives on the future of spatial biology research.

Single-cell RNA sequencing reveals myeloid and T cell co-stimulation mediated by IL-7 anti-cancer immunotherapy

BACKGROUND

Immune checkpoint inhibitors unleash inhibitory signals on T cells conferred by tumors and surrounding stromal cells. Despite the clinical efficacy of checkpoint inhibitors, the lack of target expression and persistence of immunosuppressive cells limit the pervasive effectiveness of the therapy. These limitations may be overcome by alternative approaches that co-stimulate T cells and the immune microenvironment.

METHODS

We analyzed single-cell RNA sequencing data from multiple human cancers and a mouse tumor transplant model to discover the pleiotropic expression of the Interleukin 7 (IL-7) receptor on T cells, macrophages, and dendritic cells.

RESULTS

Our experiment on the mouse model demonstrated that recombinant IL-7 therapy induces tumor regression, expansion of effector CD8 T cells, and pro-inflammatory activation of macrophages. Moreover, spatial transcriptomic data support immunostimulatory interactions between macrophages and T cells.

CONCLUSION

These results indicate that IL-7 therapy induces anti-tumor immunity by activating T cells and pro-inflammatory myeloid cells, which may have diverse therapeutic applicability.

Single-cell and single-nucleus RNA-sequencing from paired normal-adenocarcinoma lung samples provide both common and discordant biological insights

Whether single-cell RNA-sequencing (scRNA-seq) captures the same biological information as single-nucleus RNA-sequencing (snRNA-seq) remains uncertain and likely to be context-dependent. Herein, a head-to-head comparison was performed in matched normal-adenocarcinoma human lung samples to assess biological insights derived from scRNA-seq versus snRNA-seq and better understand the cellular transition that occurs from normal to tumoral tissue. Here, the transcriptome of 160,621 cells/nuclei was obtained. In non-tumor lung, cell type proportions varied widely between scRNA-seq and snRNA-seq with a predominance of immune cells in the former (81.5%) and epithelial cells (69.9%) in the later. Similar results were observed in adenocarcinomas, in addition to an overall increase in cell type heterogeneity and a greater prevalence of copy number variants in cells of epithelial origin, which suggests malignant assignment. The cell type transition that occurs from normal lung tissue to adenocarcinoma was not always concordant whether cells or nuclei were examined. As expected, large differential expression of the whole-cell and nuclear transcriptome was observed, but cell-type specific changes of paired normal and tumor lung samples revealed a set of common genes in the cells and nuclei involved in cancer-related pathways. In addition, we showed that the ligand-receptor interactome landscape of lung adenocarcinoma was largely different whether cells or nuclei were evaluated. Immune cell depletion in fresh specimens partly mitigated the difference in cell type composition observed between cells and nuclei. However, the extra manipulations affected cell viability and amplified the transcriptional signatures associated with stress responses. In conclusion, research applications focussing on mapping the immune landscape of lung adenocarcinoma benefit from scRNA-seq in fresh samples, whereas snRNA-seq of frozen samples provide a low-cost alternative to profile more epithelial and cancer cells, and yield cell type proportions that more closely match tissue content.

Immune cell-related prognostic risk model and tumor immune environment modulation in esophageal carcinoma based on single-cell and bulk RNA sequencing

BACKGROUND

Immune cells play a pivotal role in the tumor microenvironment, exerting significant influence on tumor progression and patient outcomes, but the current biomarkers are insufficient to fully capture the complex and diverse tumor immune microenvironment and the impact of immunotherapy.

METHODS

The advent of single-cell sequencing allows us to explore the tumor microenvironment at an unprecedented resolution, enabling the identification and characterization of distinct subsets of immune cells, thereby paving the way for the development of prognostic models using immune cells. Leveraging single-cell data, our study deeply investigated the intricacies of immune microenvironment heterogeneity in esophageal carcinoma.

RESULTS

We elucidated the composition, functionality, evolution, and intercellular communication patterns of immune cells, culminating in the construction of an independent prognostic model at the single-cell level. Furthermore, we conducted a comprehensive analysis of disparities in immune infiltration and immune checkpoint expression between patients categorized into high- and low-risk groups, which may impact patient prognosis.

CONCLUSION

In summary, our study harnessed multiomics data to delineate the immune profile of esophageal carcinoma patients, provide a method for leveraging molecular signatures of immune cells to identify potential biomarkers, while concurrently providing evidence for the potential benefits of immunotherapy.

PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and Caenorhabditis elegans ground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR-Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis.