Single-Cell Analysis Reveals Spatial Heterogeneity of Immune Cells in Lung Adenocarcinoma

Wang, Youyu; Li, Xiaohua; Shu, Peng; Hu, Honglin; Wang, Yuntao; Shao, Mengqi; Feng, Gang; Liu, Yu; Bai, Yifeng

doi:10.3389/fcell.2021.638374

Cited by 14 publications

(19 citation statements)

References 40 publications

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“…The PCA also showed a linear distance between the lung adenocarcinoma samples (Fig. 3), which shows spatial tumor heterogeneity, as reported by other authors by single-cell RNA-seq [20][21][22]. We observed a significant linear distance between the lung adenocarcinoma to the hIDPSCs (Fig.…”

Section: Discussionsupporting

confidence: 87%

Human Immature Dental Pulp Stem Cells Did Not Graft into a Preexisting Human Lung Adenocarcinoma

Silva¹,

Araldi

Colozza‐Gama

et al. 2022

Case Rep Oncol

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Mesenchymal stem cell (MSC)-based therapies have been considered an attractive approach for treating Huntington’s disease (HD). However, due to the pulmonary first-passage effect associated with intravenous infusion (the most commonly used route of MSC administration), there is a rising concern that the cells could be entrapped in the lungs and grafted (homing) into preexisting lung cancer. Herein, we report the case of a patient with HD enrolled in a cell therapy phase I clinical trial for HD treatment having a preexisting pulmonary nodule. The nodule was found at the trial screening. The patient was referred to a pulmonologist who considered the nodule non-cancer and authorized enrollment. The patient received four intravenous administrations of human immature dental pulp stem cells (hIDPSCs) at the dose of 1 × 10<sup>6</sup> cells/kg of body weight within 2 years. One month after the last dose, a computerized tomography scan showed nodule growth. A bronchoscopy biopsy showed primary lung adenocarcinoma. The neoplasm was surgically excised (lung superior right lobectomy). The patient is cured of the neoplasm. The tumor was sectioned into six fragments, which were subjected to RNA-seq. The transcriptome of each tumor section was compared with the transcriptome of infused hIDPSCs using two statistical approaches: principal component analysis and NOIseq. Both results demonstrated a linear distance between the hIDPSCs and the lung adenocarcinoma. These results suggest that the infused hIDPSCs neither home nor graft within the pulmonary nodule.

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Section: Discussionsupporting

confidence: 87%

Human Immature Dental Pulp Stem Cells Did Not Graft into a Preexisting Human Lung Adenocarcinoma

Silva¹,

Araldi

Colozza‐Gama

et al. 2022

Case Rep Oncol

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“…S4 (a)). As a result, by accounting for the impact of such spatial heterogeneity, models (1) and (2) yielded smaller MSEs, as compared to the model (3) across all sample sizes. As the additive noise was greatly increased, the difference in the spatial entropy curves across the two patterns was no longer recognizable.…”

Section: Simulation Studiesmentioning

confidence: 98%

“…Each dataset was partitioned into training (75%) and testing (25%) sets. Three models were fit using the training set: (1) Model accounting for both clinical predictor and spatial heterogeneity (2) Model accounting for only spatial heterogeneity, and (3) Model accounting for only clinical predictor. The estimated linear predictor was obtained from the testing set for the u th model.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…The emergence of tumor microenvironment (TME) studies has revealed a critical role of spatial heterogeneity for gaining insights into tumor initiation, development, progression, invasion, metastasis, and response to therapies (1, 2, 3, 4). The TME is known to be complex and heterogeneous due to continuous cellular and molecular adaptions in the primary tumor and its surroundings, which then allow for tumor growth and proliferation.…”

Section: Introductionmentioning

confidence: 99%

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FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures

Seal

Wrobel

et al. 2022

Preprint

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Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular architectures by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering for the spatial contribution. As such, none of the existing approaches has fully accounted for the heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage the spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular architectures. Then, functional principal component analysis (FPCA) targeting sparse data is applied to estimate FPC scores which are then predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, holding other clinical variables constant. Using an ovarian cancer dataset (n = 114) as a case study, we found that the spatial heterogeneity in the TME immune compositions of CD19+ B cells, CD4+ T cells, CD8+ T cells, and CD68+ macrophages, had a significant non-zero effect on the overall survival (p = 0.027). In the simulations studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity.

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“…More detailed information about the integration of the two has been specified in a recent paper (40), and this will be the way forward. Multi-regional scRNA-seq has also shown good applications in spatial information of lung cancer (41)(42)(43). The further exploration of spatial information of lung cancer gives us new insights into tumor heterogeneity, tumor diagnosis and treatment.…”

Section: Scrna-seq and Spatial Informationmentioning

confidence: 99%

Single-Cell RNA Sequencing in Lung Cancer: Revealing Phenotype Shaping of Stromal Cells in the Microenvironment

et al. 2022

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The lung tumor microenvironment, which is composed of heterogeneous cell populations, plays an important role in the progression of lung cancer and is closely related to therapeutic efficacy. Increasing evidence has shown that stromal components play a key role in regulating tumor invasion, metastasis and drug resistance. Therefore, a better understanding of stromal components in the tumor microenvironment is helpful for the diagnosis and treatment of lung cancer. Rapid advances in technology have brought our understanding of disease into the genetic era, and single-cell RNA sequencing has enabled us to describe gene expression profiles with unprecedented resolution, enabling quantitative analysis of gene expression at the single-cell level to reveal the correlations among heterogeneity, signaling pathways, drug resistance and microenvironment molding in lung cancer, which is important for the treatment of this disease. In this paper, several common single-cell RNA sequencing methods and their advantages and disadvantages are briefly introduced to provide a reference for selection of suitable methods. Furthermore, we review the latest progress of single-cell RNA sequencing in the study of stromal cells in the lung tumor microenvironment.

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Single-Cell Analysis Reveals Spatial Heterogeneity of Immune Cells in Lung Adenocarcinoma

Cited by 14 publications

References 40 publications

Human Immature Dental Pulp Stem Cells Did Not Graft into a Preexisting Human Lung Adenocarcinoma

Human Immature Dental Pulp Stem Cells Did Not Graft into a Preexisting Human Lung Adenocarcinoma

FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures

Single-Cell RNA Sequencing in Lung Cancer: Revealing Phenotype Shaping of Stromal Cells in the Microenvironment

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