Skip to main content

Revealing the evolution of the tumor immune microenvironment in follicular lymphoma patients progressing within 24 months using single-cell imaging mass cytometry



Patients with follicular lymphoma (FL) who experience disease progression within 24 months (POD24) have inferior outcomes. The tumor immune microenvironment (TIME) plays a crucial role in pathogenesis and progression of follicular lymphoma (FL). However, TIME evolution during progression of disease within 24 months (POD24) is elusive.


Spatially resolved and single-cell image mass cytometry with a panel of 36 metal-tagged antibodies was used to quantitatively analyze the TIME structure in 13 paired FLs at diagnosis and POD24.


Follicles and peri-follicular regions were well dissected in structure. Peri-follicular regions represented a barrier for immune infiltration into the follicles. More FL-cells in the peri-follicular regions suffered CD8+T cells attacks under simultaneous protection of regulatory T cells (Tregs) and/or macrophages compared with that in the follicles irrespective of POD24. During POD24, increased CD163 macrophages with PD-1 ligand upregulation and decreased CD8+T cells with upregulated LAG-3 expression around FL-cells were observed in the follicles. Spatial analyses demonstrated that FL-cells interacted more intimately with macrophages than with Tregs and less with cytotoxic T cells in both peri-follicular regions and follicles during POD24. In comparison, macrophages also cooperated more frequently with Tregs to simultaneously hijack FL-cells, creating an enhanced immunosuppressive environment in both peri-follicular and follicular regions during POD24.


Peri-follicular regions function as a barrier by recruiting both CD8+T cells and immunosuppressive cells, protecting follicular FL-cells from immune attack at diagnosis or POD24. FL-cells reside in a more immune-compromised microenvironment and evade immune cell attacks during POD24. Novel immunotherapeutic approaches harnessing LAG-3, macrophages, and Tregs will be empowered to overcome poor outcomes in patients with FL POD24.

To editor

Patients with follicular lymphoma (FL) who experience disease progression within 24 months (POD24) have inferior outcomes [1, 2]. Current biological understanding of POD24 is mainly based on alterations acquired at the time of diagnosis of FL [3], including gene-mutation-based prognostic model POD24-PI [4] and TIME-based model BioFLIPI [5], but biological studies on POD24 stages are rare. TIME played an important role in the progression of FL [3, 5, 6]. However, due to the lack of technologies that enable comprehensive spatial analysis at single-cell resolution with high-throughput phenotypic information, the evolution of TIME over the course of POD24 remains elusive.

Imaging mass cytometry (IMC) is a novo technology which enables the simultaneous spatial and phenotypic evaluation of up to 50 biomarkers from tissue sections stained with metal-tagged antibodies at single-cell resolution [7, 8]. Therefore, we used IMC to analyze TIME evolution from diagnosis to POD24 using 36 metal-tagged antibodies in 13 FL patients. IMC clearly identified follicles, peri-follicular regions, and the boundary in-between according to CD21/SMA/vimentin expression (Fig. 1A). A total of 12 clusters were identified in the TIME (Fig. 1B, C). Not all cells in the TIME directly interacted with FL-cells due to spatial segmentation. Therefore, analysis of neighboring components around FL-cells could reflect more accurately the microenvironment that each FL-cell resides in than cellular components in the whole sections. Macrophages (Mφs) were most abundant immunosuppressive subpopulation, which were twice as numerous as regulatory T cells (Tregs) adjacent to FL-cells at diagnosis or at POD24 (Fig. 1D, E). Furthermore, among the components that surround FL-cells, the percentage of CD163Mφs but not CD163+Mφs increased at POD24 (Fig. 1E, F). However, the frequencies of Tregs around FL-cells did not significantly change after POD24 (Fig. 1F). A decrease in CD8+ T cells around FL-cells after POD24 was observed (Fig. 1F). Further phenotypic analysis showed that CD163Mφs around FL-cells displayed higher expression of PD-L1 and PD-L2 (Fig. 1G), while CD8+T cells neighboring FL-cells showed higher expression of lymphocyte activation gene 3 but not programmed cell death protein 1(PD-1) after POD24 (Fig. 1H). However, when comparing intra-follicular TIME at diagnosis in patients with and without POD24, immune components around FL-cells were similar between two groups (Additional file 1: Fig. S1A). Imbalanced evolution in CD163Mφs and CD8+ T cells surrounding FL-cells might result in a more immunosuppressive TIME after POD24.

Fig. 1
figure 1

Evolution of immune components around FL-cells in the follicles during POD24. A The lymph node structure shows that areas between the follicular and peri-follicular boundaries were clearly displayed with SMA, vimentin, and CD21; BC Cells were divided into 12 categories including four FL subsets (clusters 1, 8, 10, and 11), two subsets of CD4+ helper T cells (Th) classified by inducible T cell costimulator (ICOS) expression including ICOSTh (cluster 2) and ICOS+Th (cluster 12), two types of tumor-associated Mφs (TAMs): CD163+Mφs (cluster 4) and CD163Mφs (cluster 6), CD8+ T cells (cluster 5); normal B cells (cluster 9); and Tregs (cluster 7) and fibrotic reticular cells (FRCs, cluster 3) according to the indicated markers displayed by the heatmap (B) and t-SNE (C). DE Pie charts of cellular components around FL-cells at diagnosis (D) and POD24 (E); F alterations in the frequency of immune cells surrounding FL-cells during POD24. G Heatmap of phenotypic alterations (left), and PD-L1/2 expression (right) in CD163 macrophages during progression of disease within 24 months (POD24) in FL. H Heatmap of phenotypic alterations (left), and PD-1 and LAG-3 expression (right) in CD8+T cells during POD24 in FL. (*p < 0.05, **p < 0.01, ***p < 0.001, ordinate represent “Expression Arcsin Ratio to Ctrl,” which were calculated as follows: (Arcsin of POD24 – arcsin of primary)/Arcsin of primary

Although the dynamic variations of FL-cells indicate the presence of immunosuppressive TIME after POD24, the evolution of spatial interactions between FL-cells and the immune cells is unknown. Eight interaction patterns were identified based on the colocalization of FL-cells with Mφs, Tregs, and CD8+T cells (Fig. 2A). Besides more exclusive interaction with Mφs in the follicular and peri-follicular regions (Pattern 4, Fig. 2B, F), more FL-cells simultaneously hijacked by Mφs and Tregs (Pattern 7) were observed in the follicles after POD24 (Fig. 2C). Furthermore, fewer FL-cells interacted with CD8+T cells (Pattern 2) without protection of immunosuppressive cells like Mφs or Tregs (Fig. 2D, E). Intriguingly, we found that peri-follicular regions represented a barrier for immune infiltration into the follicles. FL-cells in follicles were found to interact more with themselves (Additional file 1 Fig. S2A,B) and separated spatially from the attack by CD8+T cells than that in the peri-follicular regions. In contrast, FL-cells in the peri-follicular regions suffered more CD8+T cells attacks than in the follicles (Additional file 1: Fig. S2F). Notably, more FL-cells that interacted with CD8+T cells in the peri-follicular regions were simultaneously protected by Tregs and/or Mφs than in the follicles (Additional file 1: Fig. S2C). Even FL-cells distant from CD8+T cells in the outer areas were protected more frequently by the cooperation of Tregs and Mφs compared with that in the central regions (Additional file 1: Fig. S2C). Moreover, the barrier functions of peri-follicular regions were not changed after POD24 (Additional file 1: Fig. S2D, E).

Fig. 2
figure 2

Evolution of FL and immune cell interactions during POD24 in FL (A), scheme of analysis of interactions between FL and three types of immune cells. Total eight interaction patterns are proposed in the table (right) according to the colocation of FL and immune cells. The fractions of FL-cells with different interactions accounting for total FL-cells were calculated. Higher percentages of FL-cells exclusively interacting with Mφs were shown in Pattern 4 (B), while those simultaneously interacting with Mφs and Tregs were shown in Pattern 7 (C) in the follicles during POD24 in FL. A significant decrease in FL-cells exclusively interacting with CD8+T cells was shown in Pattern 2 (D) after POD24. Additionally, in the peri-follicular regions, decreased FL-cells exclusively interacting with CD8+T cells were shown in Pattern 2 (E), whereas more FL-cells exclusively interacting with Mφs were shown in Pattern 4 (F) during POD24 in FL (*p < 0.05, **p < 0.01, ***p < 0.001)

In summary, IMC was applied to create a spatially preserved single-cell-resolution immune atlas from peri-follicular to follicular regions of FL, providing novel and significant insights of TIME evolution during POD24. An enhanced immunosuppressive TIME was emerged during POD24, characterized by increased TAMs and fewer CD8+T cells, higher expression of LAG-3 around FL-cells as well as more cooperation with Tregs and TAMs around the FL-cells. Consequently, new therapeutic approaches by modulating LAG-3, TAMs, or Tregs could potentially improve dismal survival in patients with POD24.

Availability of data and materials

The original contributions presented in the study are included in the article/supplementary materials. The IMC data will be uploaded in the public repository “Zenodo”.



Follicular dendritic cells


Follicular lymphoma


Fibrotic reticular cells


Inducible T cell costimulator


Imaging mass cytometry


Lymphocyte activation gene 3




Programmed cell death protein 1


Programmed cell death protein ligand 1


Programmed cell death protein ligand 2


Progression of disease with 24 months


Regions of interest


Smooth muscle alpha actin


Tumor-associated macrophages


Conventional CD4+ T cells


Follicular helper T cells


T cell immunoglobulin and mucin domain 3


Tumor immune microenvironment


Regulatory T cells


  1. Zha J, Fan L, Yi S, Yu H, Zheng Z, Xu W, et al. Clinical features and outcomes of 1845 patients with follicular lymphoma: a real-world multicenter experience in China. J Hematol Oncol. 2021;14(1):131.

    Article  CAS  Google Scholar 

  2. Casulo C, Dixon JG, Le-Rademacher J, Hoster E, Hochster HS, Hiddemann W, et al. Validation of POD24 as a robust early clinical end point of poor survival in FL from 5225 patients on 13 clinical trials. Blood. 2022;139(11):1684–93.

    Article  CAS  Google Scholar 

  3. Tobin JWD, Keane C, Gunawardana J, Mollee P, Birch S, Hoang T, et al. Progression of disease within 24 months in follicular lymphoma is associated with reduced intratumoral immune infiltration. J Clin Oncol. 2019;37(34):3300–9.

    Article  CAS  Google Scholar 

  4. Jurinovic V, Kridel R, Staiger AM, Szczepanowski M, Horn H, Dreyling MH, et al. Clinicogenetic risk models predict early progression of follicular lymphoma after first-line immunochemotherapy. Blood. 2016;128(8):1112–20.

    Article  CAS  Google Scholar 

  5. Mondello P, Fama A, Larson MC, Feldman AL, Villasboas JC, Yang ZZ, et al. Lack of intrafollicular memory CD4 + T cells is predictive of early clinical failure in newly diagnosed follicular lymphoma. Blood Cancer J. 2021;11(7):130.

    Article  Google Scholar 

  6. Yang ZZ, Kim HJ, Villasboas JC, Price-Troska T, Jalali S, Wu H, et al. Mass cytometry analysis reveals that specific intratumoral CD4(+) T cell subsets correlate with patient survival in follicular lymphoma. Cell Rep. 2019;26(8):2178-93.e3.

    Article  CAS  Google Scholar 

  7. Schwabenland M, Salie H, Tanevski J, Killmer S, Lago MS, Schlaak AE, et al. Deep spatial profiling of human COVID-19 brains reveals neuroinflammation with distinct microanatomical microglia-T-cell interactions. Immunity. 2021;54(7):1594–610.

    Article  CAS  Google Scholar 

  8. Jackson HW, Fischer JR, Zanotelli VRT, Ali HR, Mechera R, Soysal SD, et al. The single-cell pathology landscape of breast cancer. Nature. 2020;578(7796):615–20.

    Article  CAS  Google Scholar 

Download references


The authors thank the Department of Pathology of The First Affiliated Hospital of Xiamen University for their assistance in choosing ROIs.


This work was financially supported by the National Natural Science Foundation of China (82170180 granted to Bing Xu, 81800163 granted to Jie Zha, 81800196 and 81770161 granted to Zhiming Li, 81900164 granted to Long Liu, 82100204 granted to Xingxing Yu), the Natural Science Foundation of Fujian Province (2020J011246 granted to Bing Xu), the Xiamen Municipal Bureau of Science and Technology (3502Z20209003 granted to Bing Xu, 3502Z20209008 granted for Zhifeng Li), and the Lymphoma Research Fund of the Chinese Anti-Cancer Association (CORP-117 granted to Bing Xu).

Author information

Authors and Affiliations



LL and XXY performed the experiments and data analysis. ZFL, XHH, JZ, ZJL, YH, and ZML collected lymph node samples and clinical data. HJZ, QL, and XJ contributed to antibody tests and metal conjugating. LL, XXY, KYD, GF, HFY, HYY, ZML, KHY, and BX aided in interpreting the results and worked on the manuscript. LL and BX conceived and planned the experiments. All authors have given approval to the final version of the manuscript.

Corresponding authors

Correspondence to Zhiming Li, Ken H. Young or Bing Xu.

Ethics declarations

Ethics approval and consent to participate

All samples were obtained after the patients provided informed consent. IRB approval for this study was granted by the Institutional Human Ethics Review Committee of the First Affiliated Hospital of Xiamen University and Sun Yat-sen University Cancer Center, in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1. 

Study methods and supplementary figures  and tables.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, L., Yu, X., Li, Z. et al. Revealing the evolution of the tumor immune microenvironment in follicular lymphoma patients progressing within 24 months using single-cell imaging mass cytometry. J Hematol Oncol 15, 115 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: