. 2024 Feb;14(2):e1573.

doi: 10.1002/ctm2.1573.

Spatial transcriptomics reveals heterogeneity of histological subtypes between lepidic and acinar lung adenocarcinoma

Linshan Xie¹, Hui Kong², Jinjie Yu^{3

4}, Mengting Sun¹, Shaohua Lu², Yong Zhang¹, Jie Hu¹, Fang Du⁵, Qiuyu Lian⁶, Hongyi Xin⁷, Jian Zhou^{1

8

9

10}, Xiangdong Wang^{1

11

12}, Charles A Powell¹³, Fred R Hirsch¹⁴, Chunxue Bai^{1

8

9

10}, Yuanlin Song^{1

8

9

10}, Jun Yin³, Dawei Yang^{1

8

9

10

15}

Affiliations

¹ Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
² Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China.
³ Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
⁴ Department of Thoracic Surgery, Shanghai Geriatric Medical Center, Shanghai, China.
⁵ Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China.
⁶ Gurdon Institute, University of Cambridge, Cambridge, UK.
⁷ Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China.
⁸ Shanghai Engineer and Technology Research Center of Internet of Things for Respiratory Medicine, Shanghai, China.
⁹ Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, China.
¹⁰ Shanghai Respiratory Research Institution, Shanghai, China.
¹¹ Shanghai Institute of Clinical Bioinformatics, Shanghai, China.
¹² Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Fudan University Shanghai Medical College, Shanghai, China.
¹³ Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
¹⁴ Tisch Cancer Institute, Center for Thoracic Oncology, Mount Sinai Health System, New York, New York, USA.
¹⁵ Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China.

PMID: 38318637
PMCID: PMC10844893
DOI: 10.1002/ctm2.1573

Spatial transcriptomics reveals heterogeneity of histological subtypes between lepidic and acinar lung adenocarcinoma

Linshan Xie et al. Clin Transl Med. 2024 Feb.

. 2024 Feb;14(2):e1573.

doi: 10.1002/ctm2.1573.

Authors

Affiliations

¹ Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
² Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China.
³ Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
⁴ Department of Thoracic Surgery, Shanghai Geriatric Medical Center, Shanghai, China.
⁵ Department of Anesthesiology, Zhongshan Hospital, Fudan University, Shanghai, China.
⁶ Gurdon Institute, University of Cambridge, Cambridge, UK.
⁷ Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China.
⁸ Shanghai Engineer and Technology Research Center of Internet of Things for Respiratory Medicine, Shanghai, China.
⁹ Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, China.
¹⁰ Shanghai Respiratory Research Institution, Shanghai, China.
¹¹ Shanghai Institute of Clinical Bioinformatics, Shanghai, China.
¹² Shanghai Engineering Research for AI Technology for Cardiopulmonary Diseases, Fudan University Shanghai Medical College, Shanghai, China.
¹³ Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
¹⁴ Tisch Cancer Institute, Center for Thoracic Oncology, Mount Sinai Health System, New York, New York, USA.
¹⁵ Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China.

PMID: 38318637
PMCID: PMC10844893
DOI: 10.1002/ctm2.1573

Abstract

Background: Patients who possess various histological subtypes of early-stage lung adenocarcinoma (LUAD) have considerably diverse prognoses. The simultaneous existence of several histological subtypes reduces the clinical accuracy of the diagnosis and prognosis of early-stage LUAD due to intratumour intricacy.

Methods: We included 11 postoperative LUAD patients pathologically confirmed to be stage IA. Single-cell RNA sequencing (scRNA-seq) was carried out on matched tumour and normal tissue. Three formalin-fixed and paraffin-embedded cases were randomly selected for 10× Genomics Visium analysis, one of which was analysed by digital spatial profiler (DSP).

Results: Using DSP and 10× Genomics Visium analysis, signature gene profiles for lepidic and acinar histological subtypes were acquired. The percentage of histological subtypes predicted for the patients from samples of 11 LUAD fresh tissues by scRNA-seq showed a degree of concordance with the clinicopathologic findings assessed by visual examination. DSP proteomics and 10× Genomics Visium transcriptomics analyses revealed that a negative correlation (Spearman correlation analysis: r = -.886; p = .033) between the expression levels of CD8 and the expression trend of programmed cell death 1(PD-L1) on tumour endothelial cells. The percentage of CD8+ T cells in the acinar region was lower than in the lepidic region.

Conclusions: These findings illustrate that assessing patient histological subtypes at the single-cell level is feasible. Additionally, tumour endothelial cells that express PD-L1 in stage IA LUAD suppress immune-responsive CD8+ T cells.

Keywords: digital spatial profiler; histological subtypes; lung adenocarcinoma; single-cell RNA sequencing; tumour endothelial cells.

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Conflict of interest statement

The authors declare they have no conflicts of interest.

Figures

**FIGURE 1**
Digital spatial profiler (DSP) in combination with single‐cell RNA sequencing (scRNA‐seq) technology to select lepidic and acinar signature genes. (A) Pathologic haematoxylin and eosin (H&E) staining of the patient (left, original magnification ×1), and the circled regions of normal, lepidic and acinar subtypes (right, original magnification ×10). (B) Fluorescence staining of the patient (left, original magnification ×1), and the circled regions of normal, lepidic and acinar subtypes using the DSP technique (right, original magnification ×10). PanCK+(Green), CD31+(Red), CD45+(Yellow) and SYTO13 (Blue). (C) Regions of interest (ROIs) of normal, lepidic and acinar cells identified using 10× Genomics Visium. (D) Percentage of pathologic subtypes in 11 lung adenocarcinoma patients. Reassessment of the percentage of lepidic and subtypes in patients at the single‐cell level.

**FIGURE 2**
Acquire lepidic and acinar signature gene sets. (A) Heatmap of gene expression using unsupervised clustering for PanCK+ area of illuminations (AOIs) (n = 6). Heatmaps are annotated by histological region. (B) Selection of lepidic and acinar epithelial signature genes combined with digital spatial profiler (DSP) and 10× Genomics Visium. (C) Expression levels of representative lepidic and acinar signature genes in the lepidic and acinar regions. (D) Gene ontology (GO) pathway of lepidic and acinar signature genes enrichment. ACI, acinar; GSTA1, glutathione S‐transferase alpha 1; LEP, lepidic; N, normal; TNC, tenascin C.

**FIGURE 3**
Lepidic and acinar signature gene sets predict histological subtypes and prognosis. (A) Uniform Manifold Approximation and Projection (UMAP) plot of 215 200 single cells from 11 patients, coloured according to their 10 major cell types. (B) UMAP of epithelial cells coloured according to the lepidic and acinar signature genes. (C) Single‐cell RNA sequencing (scRNA‐seq) predictions of histological subtypes fitting clinicopathologic results. (D) Kaplan–Meier curves of stage I lung adenocarcinoma patients (GSE31210). (E) Kaplan–Meier curves of stage I lung adenocarcinoma patients (GSE42127). (F) Kaplan–Meier curves of stage I lung adenocarcinoma patients (GSE50081). Time = year.

**FIGURE 4**
Endothelial cells promote early‐stage lung adenocarcinoma (LUAD) progression. (A) Gene ontology (GO) pathway enrichment of genes characterised by lepidic and acinar endothelial cells. (B) GO pathway enrichment of genes characterised by lepidic and acinar immune cells. (C) Mean CD8 protein expression levels in CD45+ area of illuminations (AOIs) in digital spatial profiler (DSP) analysis. (D) Mean PD‐L1 protein expression levels in CD31+ AOI in DSP analysis. (E) Mean PD‐L1 protein expression levels in PanCK+ AOI in DSP analysis. (F) Spearman correlation analysis of CD8 expression in CD45+ AOI with PD‐L1 expression in CD31+ AOI in DSP. (G) Spearman correlation analysis of CD8 expression in CD45+ AOI with PD‐L1 expression in PanCK+ AOI in DSP. (H) Percentage of CD8+ T cells in lepidic region (n = 8) and acinar region (n = 8) using 10× Genomics Visium. ^* p < .05.

**FIGURE 5**
Interactions between epithelial, endothelial and immune cells in lepidic and acinar subtypes. (A) Representative multiplex immunofluorescence images from normal (n = 8), lepidic (n = 10) and acinar (n = 9) regions from three patients. (B) Measurement of CD8+ cells in lung adenocarcinoma (LUAD). *Note*: The denominator is the total number of cells in the region. (C) Measurement of PD‐L1+/CD31+ cells in LUAD. (D) Measurement of PDL1+/PanCK+ cells in LUAD. (E) In the lepidic histological subtype microenvironment, endothelial cells underexpressed PD‐L1 and abundantly recruited CD8+ T cells for infiltration, whereas in the acinar histological subtype tumour microenvironment, endothelial cells expressed PD‐L1 and inhibited CD8+ T‐cell infiltration.

See this image and copyright information in PMC

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Spatial transcriptomics reveals heterogeneity of histological subtypes between lepidic and acinar lung adenocarcinoma

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Spatial transcriptomics reveals heterogeneity of histological subtypes between lepidic and acinar lung adenocarcinoma

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