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Intertumoral heterogeneity of tumor epithelial cells in lung adenocarcinomas. A UMAPs based on the top 20 principal components of all epithelial single-cell transcriptomes color-coded by tissue type, cell type and patient, and quantification of epithelial cell types per tissue type, AT1, alveolar type 1 cells, AT2, alveolar type 2 cells. B Average gene expression of selected marker genes for normal epithelial cell types. C Differentially expressed genes in tumor epithelial cells grouped by patients, maximum top ten genes showed per patient, for patient color code see (A). D Immunohistochemical staining of proteins encoded by selected differentially expressed genes indicated by black arrowheads in (C). E Mean pathway activity scores of tumor epithelial cells grouped by patient. F Distribution of histological subtypes, (G) mean module scores of normal epithelial cell type gene signatures, and (H) mean pathway activity scores of tumor epithelial cells sorted along principal component 1 (PC1). F, G, H Principal component analysis based on gene expression of all tumor epithelial single-cell transcriptomes; schematic depiction of tumor cell signature module scores along PC1.
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Recent developments in immuno-oncology demonstrate that not only cancer cells, but also the tumor microenvironment can guide precision medicine. A comprehensive and in-depth characterization of the tumor microenvironment is challenging since its cell populations are diverse and can be important even if scarce. To identify clinically relevant microe...
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... to unsorted singlecell RNA sequencing, yielding 114,489 high-quality transcriptomes after quality control and filtering (Fig. 1A, Supplementary Fig. 1A, B). Evaluation of consecutive H&E stained tissue sections showed tumor morphology ranging from well differentiated lepidic to poorly differentiated sarcomatoid growth patterns (Supplementary Fig. ...
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... as normal or tumor cell clusters, based on tissue origin ( Supplementary Fig. 3B), which was largely congruent with the copy-number status of cells ( Supplementary Fig. 4A, B), and demonstrated a tumor purity >90%. Within the normal cell clusters, we found alveolar type 1 and 2, club, ciliated, and even a small cluster of neuroendocrine cells ( Fig. 2A), which were characterized by expression of typical individual marker genes ( [13,14]. The club cell cluster also expressed basal cell marker genes such as NGFR and KRT5 indicating an admixture of small amounts of basal cells (Fig. 2B). Tumor cell clusters segregated from normal cell clusters and were mainly patient-specific, ...
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... cell clusters, we found alveolar type 1 and 2, club, ciliated, and even a small cluster of neuroendocrine cells ( Fig. 2A), which were characterized by expression of typical individual marker genes ( [13,14]. The club cell cluster also expressed basal cell marker genes such as NGFR and KRT5 indicating an admixture of small amounts of basal cells (Fig. 2B). Tumor cell clusters segregated from normal cell clusters and were mainly patient-specific, indicating intertumoral heterogeneity ( Fig. 2A). This was underlined by a variety of genes differentially expressed across tumors such as EGFR, TFF3, CDKN2A, and SFTPA2 (Fig. 2C, black arrowheads), correlating with protein expression as shown ...
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... by expression of typical individual marker genes ( [13,14]. The club cell cluster also expressed basal cell marker genes such as NGFR and KRT5 indicating an admixture of small amounts of basal cells (Fig. 2B). Tumor cell clusters segregated from normal cell clusters and were mainly patient-specific, indicating intertumoral heterogeneity ( Fig. 2A). This was underlined by a variety of genes differentially expressed across tumors such as EGFR, TFF3, CDKN2A, and SFTPA2 (Fig. 2C, black arrowheads), correlating with protein expression as shown by immunostaining (Fig. 2D). We quantified oncogenic signal strengths by pathway target gene signature expression and found highly variable ...
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... as NGFR and KRT5 indicating an admixture of small amounts of basal cells (Fig. 2B). Tumor cell clusters segregated from normal cell clusters and were mainly patient-specific, indicating intertumoral heterogeneity ( Fig. 2A). This was underlined by a variety of genes differentially expressed across tumors such as EGFR, TFF3, CDKN2A, and SFTPA2 (Fig. 2C, black arrowheads), correlating with protein expression as shown by immunostaining (Fig. 2D). We quantified oncogenic signal strengths by pathway target gene signature expression and found highly variable activities for EGFR, TGFβ, JAK/STAT, Hypoxia, and PI3K signaling across the different patients (Fig. 2E). These signal strengths ...
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... cell clusters segregated from normal cell clusters and were mainly patient-specific, indicating intertumoral heterogeneity ( Fig. 2A). This was underlined by a variety of genes differentially expressed across tumors such as EGFR, TFF3, CDKN2A, and SFTPA2 (Fig. 2C, black arrowheads), correlating with protein expression as shown by immunostaining (Fig. 2D). We quantified oncogenic signal strengths by pathway target gene signature expression and found highly variable activities for EGFR, TGFβ, JAK/STAT, Hypoxia, and PI3K signaling across the different patients (Fig. 2E). These signal strengths were largely unrelated to the mitotic activity of tumor epithelial cells (Supplementary Fig. ...
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... such as EGFR, TFF3, CDKN2A, and SFTPA2 (Fig. 2C, black arrowheads), correlating with protein expression as shown by immunostaining (Fig. 2D). We quantified oncogenic signal strengths by pathway target gene signature expression and found highly variable activities for EGFR, TGFβ, JAK/STAT, Hypoxia, and PI3K signaling across the different patients (Fig. 2E). These signal strengths were largely unrelated to the mitotic activity of tumor epithelial cells (Supplementary Fig. 3C). p53 signaling was significantly reduced in tumors harboring TP53 mutations, whereas pathway activity scores for EGFR and MAPK signaling were not significantly higher in KRAS-mutated compared to KRAS-wildtype tumors ...
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... patient subgroups. In order to emphasize similarities between tumors, epithelial transcriptomes were embedded in lowdimensional UMAPs (4, 6, 8 instead of 20 dimensions). Here, tumor cells clustered by histological subtype rather than by Supplementary Fig. 3E), and the first principal component (PC1) displayed a gradient along histological grades (Fig. 2F, Supplementary Fig. 3E, F). Interestingly, SCGB3A1 and SCGB3A2 (Fig. 2C, white arrowheads), two genes that were previously associated with lung development [15], were positively correlated with PC1 ( Supplementary Fig. 3G, arrowheads). Moreover, gene signature scores of normal lung cell types [13] along PC1 showed a strong positive ...
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... transcriptomes were embedded in lowdimensional UMAPs (4, 6, 8 instead of 20 dimensions). Here, tumor cells clustered by histological subtype rather than by Supplementary Fig. 3E), and the first principal component (PC1) displayed a gradient along histological grades (Fig. 2F, Supplementary Fig. 3E, F). Interestingly, SCGB3A1 and SCGB3A2 (Fig. 2C, white arrowheads), two genes that were previously associated with lung development [15], were positively correlated with PC1 ( Supplementary Fig. 3G, arrowheads). Moreover, gene signature scores of normal lung cell types [13] along PC1 showed a strong positive correlation with gene expression profiles of alveolar type 1 and 2 as well ...
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... arrowheads), two genes that were previously associated with lung development [15], were positively correlated with PC1 ( Supplementary Fig. 3G, arrowheads). Moreover, gene signature scores of normal lung cell types [13] along PC1 showed a strong positive correlation with gene expression profiles of alveolar type 1 and 2 as well as club cells (Fig. 2G). Together, this indicates that PC1 reflects the degree of differentiation of tumor epithelial cells. Hence, the top 30 genes positively and negatively correlated with PC1 were defined as an "alveolar/club-like" and "undifferentiated" tumor cell signature, respectively ( Fig. 2F-H, Supplementary Fig. 3G, H). While tumor cells with ...
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... expression profiles of alveolar type 1 and 2 as well as club cells (Fig. 2G). Together, this indicates that PC1 reflects the degree of differentiation of tumor epithelial cells. Hence, the top 30 genes positively and negatively correlated with PC1 were defined as an "alveolar/club-like" and "undifferentiated" tumor cell signature, respectively ( Fig. 2F-H, Supplementary Fig. 3G, H). While tumor cells with different degrees of differentiation exhibited no clear differences in mitotic activity ( Supplementary Fig. 3I), we found high pathway activity scores for JAK/STAT, Hypoxia, EGFR and TGFβ signaling in "undifferentiated", and high scores for PI3K signaling in "alveolar/club cell-like" ...
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... While tumor cells with different degrees of differentiation exhibited no clear differences in mitotic activity ( Supplementary Fig. 3I), we found high pathway activity scores for JAK/STAT, Hypoxia, EGFR and TGFβ signaling in "undifferentiated", and high scores for PI3K signaling in "alveolar/club cell-like" tumor epithelial cells, respectively (Fig. 2H). We conclude that tumor epithelial cells of different lung adenocarcinoma patients exhibit transcriptional patterns along a spectrum ranging from undifferentiated to alveolar/club cell-like phenotypes correlating with distinct oncogenic pathway ...
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Although the value of tumor-infiltrating lymphocytes is well known, the clinical relevance of an increased immune response, specifically in breast cancer, has not been investigated across large cohorts of patients using computational algorithms. Our hypothesis stated that an enhanced immune response is associated with an improvement in outcomes. To...
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... UMAP colored by FACS-sorting label showed clear separation of CD45 positive and negative cells as expected ( Figure 5G). We detected all major specialized lung epithelial and infiltrating stromal and immune cell phenotypes featuring lung adenocarcinoma disease (Figure 5E, H), and coinciding with previous reports [7,58,59]. Also, we observed high inter-patient variability . CC-BY-NC-ND 4.0 International license perpetuity. ...
... Consistent with previous LUAD atlases [7,58,59] and CXCL13-high CD4 T cells (Figure 5E). Interestingly, the tumor sample comprising CXCL13-high T cell phenotype coincided with high count of B cells, therefore, supporting recent findings that CXCL13 acts as a potent attractant for B and other immune cells [65]. ...
... As a proof-of-concept we performed multiregional profiling of lung carcinomas by hash-tagging 18 samples that were preserved after acquiring them from 3 patients. We could clearly differentiate the patients based on their disease profile ( Figure S5) and identified all major specialized lung epithelial, infiltrating stromal and immune cell phenotypes [7,58,59]. We also identified cells with potential significance in immunotherapy, such as the CXCL13 producing CD4 T cells. ...
The development of a large variety of single-cell analytical methods has empowered researchers to explore diverse biological questions at the level of individual cells. Among these, droplet-based single-cell RNA sequencing (scRNA-seq) methods have been particularly prevalent owning to their high-throughput capabilities and reduced reaction volumes. While commercial systems have contributed to the widespread adoption of droplet-based scRNA-seq, the relatively high cost impose limitations for profiling large numbers of samples. Moreover, as the scope and scale of single cell sequencing methods keeps expanding, the possibility to accommodate diverse molecular biology workflows and inexpensively profile multiple biospecimens simultaneously, becomes highly relevant. Herein, we present inDrops-2: an open-source scRNA-seq platform designed to profile fresh or preserved clinical samples with a sensitivity matching that of state-of-the-art commercial systems, yet at the few folds lower cost. Using inDrops-2, we conducted a comparative analysis of two prominent scRNA-seq protocols - those based on exponential and linear amplification of cDNA - and provide new insights about the technical biases inherited to each approach. Finally, we applied inDrops-2 to simultaneously profile 18 lung carcinoma samples, all in one run following cell dehydration, long-term storage and multiplexing, to obtain a multiregional cellular profile of tumor microenvironment; thus, inDrops-2 offers researchers a possibility to perform large scale transcriptomics studies in a cost-effective manner.
... It has been well appreciated that TME, providing the crucial intercellular platform, closely affects tumorigenesis, progression and long-term prognosis by modulating the functional and genetic alteration in cell subpopulations [8][9][10]. In the last decades, overwhelming evidence suggests that differentially expressed gene (DEG) in the TME are not only promising biomarkers for prognostic evaluation including metastasis assessment, but also act as effective targets for cancer treatment [11][12][13]. ...
Background:
Metastasis of lung adenocarcinoma (LUAD) severely worsens prognosis. Genetic alteration in the tumor microenvironment (TME) is closely associated with metastasis and other malignant biological properties of LUAD. In this study, we establish a metastasis-related risk model to accurately predict LUAD prognosis.
Methods:
RNA-sequencing profiles and clinical data of LUAD patients including 503 tumor tissues and 54 adjacent normal tissues were collected in TCGA database. Additionally, the paired specimens from 156 LUAD patients were obtained in a single center. The metastatic relevance and clinical significance of metastasis-related long non-coding RNA (MRLNRs) was validated by series of in vitro experiments including western blotting, qPCR and transwell assays.
Results:
Six MRLNRs were significantly correlated to prognoses of LUAD patients, of which AL359220.1, SH3BP5-AS1 and ZF-AS1 were further used to establish a metastasis-related risk scoring model (MRRS) due to the close associations with overall survival of LUAD patients. According to the MRRS, patients with higher scores in the high-risk group obtained poorer prognoses and survival outcomes. ZFAS1 expressed highly in tumor tissues and showed the inverse results compared to SH3BP5-AS1 and AL359220.1. In addition, the high expression of ZFAS1 was prominently correlated to the more advanced T-stage and distant metastasis. The reduction of ZFAS1 induced by siRNAs dramatically diminished the migration and invasion abilities of LUAD cells.
Conclusions:
In the present research, we elucidate the metastatic relevance and clinical significance of AL359220.1, SH3BP5-AS1 and ZF-AS1 in LUAD. Moreover, MRRS provide a promising assessing model for clinical decision making and prognosis of LUAD.
... Single-cell transcriptomics has revolutionised our understanding of the tumour microenvironment, providing a wealth of data on nearly all cell types at single-cell resolution. However, until recently, the majority of scRNAseq studies failed to recover a significant number of neutrophils for downstream analysis; this is a feature also prominent in other single-cell analyses performed in solid organs [22][23][24][25][26]. Reasons behind this include the fragile nature of neutrophils during isolation and the relative low abundance of RNA compared with other cell types. ...
Neutrophils, until recently, have typically been considered a homogeneous population of terminally differentiated cells with highly conserved functions in homeostasis and disease. In hepatocellular carcinoma (HCC), tumour-associated neutrophils (TANs) are predominantly thought to play a pro-tumour role, promoting all aspects of HCC development and progression. Recent developments in single-cell technologies are now providing a greater insight and appreciation for the level of cellular heterogeneity displayed by TANs in the HCC tumour microenvironment, which we have been able to correlate with other TAN signatures in datasets for gastric cancer, pancreatic ductal adenocarcinoma (PDAC) and non-small cell lung cancer (NSCLC). TANs with classical pro-tumour signatures have been identified as well as neutrophils primed for anti-tumour functions that, if activated and expanded, could become a potential therapeutic approach. In recent years, therapeutic targeting of neutrophils in HCC has been typically focused on impairing the recruitment of pro-tumour neutrophils. This has now been coupled with immune checkpoint blockade with the aim to stimulate lymphocyte-mediated anti-tumour immunity whilst impairing neutrophil-mediated immunosuppression. As a result, neutrophil-directed therapies are now entering clinical trials for HCC. Pharmacological targeting along with ex vivo reprogramming of neutrophils in HCC patients is, however, in its infancy and a greater understanding of neutrophil heterogeneity, with a view to exploit it, may pave the way for improved immunotherapy outcomes. This review will cover the recent developments in our understanding of neutrophil heterogeneity in HCC and how neutrophils can be harnessed to improve HCC immunotherapy.
... The frequency of some cell types exhibited remarkable heterogeneity along the course of normal lung to preneoplasia and invasive LUAD (Fig. S2, Fig. 1C). For example, the increased relative proportion of T cells and the decline in NK cells were observed from normal lung to IA; the fraction of epithelial cells varied by histological subtype, as we observed a relatively higher frequency in tumors with lepidic pattern, but a lower frequency in tumors harboring solid/papillary pattern, and which are in line with previous observations [25]. These observations highlight transcriptomic heterogeneity in the tumor microenvironment during the malignant progression of LUAD. ...
... This study provides new insights into the mechanism underlying the CD4−c10 Mean expression progression of LUAD from the perspective of the formation of immunosuppressive TME. As with previous scRNA-seq studies [25,28], we observed that the proportion of epithelial cells was lower than that of immune cells, suggesting a possibility that epithelial cell transcriptomes might be underestimated by scRNA-seq due to well-known disparities in dissociation efficiency of different cell types following tissue disaggregation [41]. Another possibility might be the tissues we used in the present study were mainly earlystage LUAD samples, showing a relatively smaller composition of epithelial cells compared to other solid tumors [42,43]. ...
... Our scRNA-seq data showed that T lymphocytes and myeloid cells accounted for the majority of immune cells, in line with previous studies [25,28]. After re-clustering T cells and myeloid cells, we observed dramatic increases in the fractions of CD4 + NR4A3 and TAM-FOLR2 cells in IA relative to those in other stages. ...
An immunosuppressive microenvironment enriched with regulatory CD4 ⁺ T lymphocytes (Tregs) facilitates the progression of lung adenocarcinoma (LUAD). This study aims to investigate the cellular mechanism underlying the formation of the immunosuppressive microenvironment in LUAD. LUAD samples ( n = 12) and normal lung samples ( n = 3) were obtained from patients with different pathological stages of LUAD. Single-cell RNA sequencing was performed to classify cellular components and analyze the transcriptomes, including transcription factors/targets and chemokine ligands/receptors, followed by bioinformatics study such as pseudotime analysis. Myeloid cells and T cells were the most abundant cell types in tumors and normal lung tissues, while tumor-associated macrophage-folate receptor 2 (TAM-FOLR2) and CD4 ⁺ nuclear receptor subfamily 4 group A member 3 (NR4A3) exhibited sharp increases in invasive adenocarcinoma (IA). The enrichment of TAM-FOLR2 in IA might result from alveolar resident macrophage-resistin (ARM-RETN) transformation and recruitment of dendritic cells (DCs) and other TAMs, as evidenced by temporal trajectories and differential expression profiles of chemokine ligands/receptors versus those in the early stages of tumors. High expression of CCL17/19/22 was observed in IA as well as in DCs, along with the strong interaction of TAM-FOLR2 with DCs. The results of pseudotime analysis suggested that CD4 ⁺ NR4A3 might potentially convert to CD4 ⁺ FOXP3, further supported by the high expression of NR4A3 target genes in CD4 ⁺ FOXP3 cells. This study provides a single-cell transcriptome atlas from preinvasive to invasive LUAD and reveals a potential ARM-RETN/TAM-FOLR2/DCs/CD4 ⁺ NR4A3/CD4 ⁺ FOXP3 trajectory in shaping the immune suppressive microenvironment along the pathogenesis of LUAD.
... We obtained single-cell sequencing data from 10 surgically resected primary LAUDs (73,566 cells) without specific treatment [36]. After strict quality control (removing nFeature > 10,000 or 500, nCount > 100,000 or 1,000, mitochondrial gene > 30%, and erythrocyte gene > 5%), 62115 high-quality cells were obtained. ...
Cancer-associated fibroblasts (CAFs) are an important component of the tumor microenvironment (TME). CAFs can promote tumor occurrence and metastasis by promoting cancer cell proliferation, angiogenesis, extracellular matrix (ECM) remodeling, and drug resistance. Nevertheless, how CAFs are related to Lung adenocarcinoma (LUAD) has not yet been revealed, especially since the CAFs-related prediction model has yet to be established. We combined Single-cell RNA-sequencing (scRNA-seq) and Bulk-RNA data to develop a predictive model of 8 CAFs-associated genes. Our model predicted LUAD prognosis and immunotherapy efficacy. TME, mutation landscape and drug sensitivity differences were also systematically analyzed between the LUAD patients of high- and low-risk. Moreover, the model prognostic performance was validated in four independent validation cohorts in the Gene expression omnibus (GEO) and the IMvigor210 immunotherapy cohort.
... Characterization of TECs in other scRNA-seq studies of human lung tumours is relatively scarce 124,125,136 . Nonetheless, a consistent downregulation of genes involved in immune activation was reported in ACKR1 + IGFBP3 + TECs and SPRY1 + TECs 124,125 , reinforcing the hypothesis that TECs may have a role in promoting immune tolerance in lung tumours. ...
Anti-angiogenic therapies (AATs) are used to treat different types of cancers. However, their success is limited owing to insufficient efficacy and resistance. Recently, single-cell omics studies of tumour endothelial cells (TECs) have provided new mechanistic insight. Here, we overview the heterogeneity of human TECs of all tumour types studied to date, at the single-cell level. Notably, most human tumour types contain varying numbers but only a small population of angiogenic TECs, the presumed targets of AATs, possibly contributing to the limited efficacy of and resistance to AATs. In general, TECs are heterogeneous within and across all tumour types, but comparing TEC phenotypes across tumours is currently challenging, owing to the lack of a uniform nomenclature for endothelial cells and consistent single-cell analysis protocols, urgently raising the need for a more consistent approach. Nonetheless, across most tumour types, universal TEC markers (ACKR1, PLVAP and IGFBP3) can be identified. Besides angiogenesis, biological processes such as immunomodulation and extracellular matrix organization are among the most commonly predicted enriched signatures of TECs across different tumour types. Although angiogenesis and extracellular matrix targets have been considered for AAT (without the hoped success), the immunomodulatory properties of TECs have not been fully considered as a novel anticancer therapeutic approach. Therefore, we also discuss progress, limitations, solutions and novel targets for AAT development.
... We used PCA (dimensions 1:40) to reduce the dimensionality of each dataset. Data acquisition source: Supplementary Table 2 and lung scRNA datasets 68,69,[85][86][87][88][89][90][91][92][93][94][95][96][97][98][99][100][101] . ...
Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce ‘bridge integration’, a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a ‘dictionary’, which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit (http://www.satijalab.org/seurat), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities.
... It was reported that LCAM enrichment enhances the immunotherapeutic response in NSCLC. Another study has also defined an immune-activated microenvironment called CP²E, which is composed of cancerassociated fibroblasts, macrophages derived from pro-inflammatory monocytes, plasma dendritic cells, and exhausted CD8 + T cells, which is associated with poor prognosis in lung cancer (129). These findings highlight the importance of considering multiple cell types in the TME when developing immunotherapies and suggest potential targets for future research (Figure 3). ...
Immunotherapy has made great strides in the treatment of lung cancer, but a significant proportion of patients still do not respond to treatment. Therefore, the identification of novel targets is crucial to improving the response to immunotherapy. The tumor microenvironment (TME) is a complex niche composed of diverse pro-tumor molecules and cell populations, making the function and mechanism of a unique cell subset difficult to understand. However, the advent of single-cell RNA sequencing (scRNA-seq) technology has made it possible to identify cellular markers and understand their potential functions and mechanisms in the TME. In this review, we highlight recent advances emerging from scRNA-seq studies in lung cancer, with a particular focus on stromal cells. We elucidate the cellular developmental trajectory, phenotypic remodeling, and cell interactions during tumor progression. Our review proposes predictive biomarkers and novel targets for lung cancer immunotherapy based on cellular markers identified through scRNA-seq. The identification of novel targets could help improve the response to immunotherapy. The use of scRNA-seq technology could provide new strategies to understand the TME and develop personalized immunotherapy for lung cancer patients.
... We investigated whether patient-specific differences in tumor and microenvironmental characteristics can be determined in fresh and FFPE tissue samples to the same degree. The histological grade of tumors (Fig. 4A) perfectly correlated with recently identified gene signatures of tumor differentiation 16 (Fig. 4B), regardless of whether the signatures were called from fresh or FFPE tissue-derived epithelial cell transcriptomes. The lowest histological tumor grade in patient P079 was further associated with higher activity of CAF-related pathways in fibroblasts 17 (Fig. 4C) and high expression of typical CAF marker genes on the single-cell mRNA level (Fig. 4D). ...
... Among T cells, high PD-L1 expression in P079 correlated with low cytotoxicity and high naiveness scores in CD4+ T cells, and high scores for exhaustion in CD8+ T cells (Fig. 4I) 21 . In summary, these correlations are in agreement with previous analysis of the lung cancer microenvironment 16,22 , and indicate that cell type characteristics relevant for clinical stratification can be retrieved faithfully from the FFPE snRNA-seq approach. ...
Single-cell transcriptional profiling reveals cell heterogeneity and clinically relevant traits in intra-operatively collected patient-derived tissue. However, the established approach to perform such analyses on freshly collected tissue constitutes an important limitation since it requires prospective collection and immediate processing. Therefore, the ability to perform single-cell RNA sequencing from archived tissues would be very beneficial in a clinical setting. Here, we benchmark single-cell gene expression profiles from patient-matched fresh, cryopreserved and FFPE cancer tissue. We find that fresh tissue and FFPE routine blocks can be employed for the robust detection of clinically relevant traits on the single-cell level. Specifically, single-cell maps of fresh patient tissues and corresponding FFPE tissue blocks could be integrated into common low-dimensional representations, and cell subtype clusters showed highly correlated transcriptional strengths of signaling pathways, Hallmark and clinically useful signatures, despite some variability in expression of individual genes due to technological differences. FFPE tissue blocks revealed higher cell diversity compared to fresh tissue. In contrast, single-cell profiling of cryopreserved tissue was prone to artifacts in the clinical setting. Our analysis suggests that single-cell RNA sequencing from FFPE tissues is comparable to and can replace analyses from fresh tissue. This highlights the potential of single-cell profiling in the analysis of retrospectively and prospectively collected archival pathology cohorts and dramatically increases the applicability in translational projects.
... Tumor-associated macrophages (TAMs) have increasingly been recognized as predicting a lung adenocarcinoma (LUAD) prognosis [21]. The tumor microenvironment of LUAD is complex, including the immune activation microenvironment and the immune suppression microenvironment [22]. In these two different tumor microenvironments, macrophages with different functions play a central role in the heterogeneity of the LUAD immune microenvironment [5]. ...
Lung cancer is a highly heterogeneous disease. Cancer cells and other cells within the tumor microenvironment interact to determine disease progression, as well as response to or escape from treatment. Understanding the regulatory relationship between cancer cells and their tumor microenvironment in lung adenocarcinoma is of great significance for exploring the heterogeneity of the tumor microenvironment and its role in the genesis and development of lung adenocarcinoma. This work uses public single-cell transcriptome data (distant normal, nLung; early LUAD, tLung; advanced LUAD, tL/B), to draft a cell map of lung adenocarcinoma from onset to progression, and provide a cell-cell communication view of lung adenocarcinoma in the different disease stages. Based on the analysis of cell populations, it was found that the proportion of macrophages was significantly reduced in the development of lung adenocarcinoma, and patients with lower proportions of macrophages exhibited poor prognosis. We therefore constructed a process to screen an intercellular gene regulatory network that reduces any error generated by single cell communication analysis and increases the credibility of selected cell communication signals. Based on the key regulatory signals in the macrophage-tumor cell regulatory network, we performed a pseudotime analysis of the macrophages and found that signal molecules (TIMP1, VEGFA, SPP1) are highly expressed in immunosuppression-associated macrophages. These molecules were also validated using an independent dataset and were significantly associated with poor prognosis. Our study provides an effective method for screening the key regulatory signals in the tumor microenvironment and the selected signal molecules may serve as a reference to guide the development of diagnostic biomarkers for risk stratification and therapeutic targets for lung adenocarcinoma.
