Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma

Gaglia, Giorgio; Burger, M.; Ritch, Cecily C.; Rammos, Danae; Dai, Yang; Crossland, Grace E.; Tavana, Sara; Warchol, Simon; Jaeger, Alex M.; Naranjo, Santiago; Coy, Shannon; Nirmal, Ajit J.; Krueger, Robert F.; Lin, Jia-Ren; Pfister, Hanspeter; Sorger, Peter K.; Jacks, Tyler; Santagata, Sandro

doi:10.1016/j.ccell.2023.03.015

Cited by 34 publications

(16 citation statements)

References 57 publications

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“…Since the intratumoral TLS that predict the better prognosis are predominantly mature TLS, investigating if TLS maturation could be the confounding reason for the difference in prognostic associations in ccRCC rather than the localization constitutes a possible goal for future research. Evidences suggest that E‐TLS may be initiated from T cell factor 1 (TCF‐1) ‐expressing stem‐like T cells, which may interact with CD4 + CXCL13 + T helper cells and consequently attract B cells 42,43 . However, E‐TLS lack sufficient antigen presentation and therefore specific antitumor response are obstructed.…”

Section: Discussionmentioning

confidence: 99%

Unveiling the impact of tertiary lymphoid structures on immunotherapeutic responses of clear cell renal cell carcinoma

Xu,

Lu,

Tian

et al. 2024

MedComm

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Tertiary lymphoid structures (TLS) are organized aggregates of immune cells that form under pathological conditions. However, the predictive value of TLS in clear cell renal cell carcinoma (ccRCC) for immunotherapies remains unclear. We comprehensively assessed the implications for prognosis and immunological responses of the TLS spatial and maturation heterogeneity in 655 ccRCC patients. A higher proportion of early‐TLS was found in peritumoral TLS, while intratumoral TLS mainly comprised secondary follicle‐like TLS (SFL‐TLS), indicating markedly better survival. Notably, presence of TLS, especially intratumoral TLS and SFL‐TLS, significantly correlated with better survival and objective reflection rate for ccRCC patients receiving anti‐Programmed Cell Death Protein‐1 (PD‐1)/Programmed Cell Death‐Ligand‐1 (PD‐L1) immunotherapies. In peritumoral TLS cluster, primary follicle‐like TLS, the proportion of tumor‐associated macrophages, and Treg infiltration in the peritumoral regions increased prominently, suggesting an immunosuppressive tumor microenvironment. Interestingly, spatial transcriptome annotation and multispectral fluorescence showed that an abundance of mature plasma cells within mature TLS has the capacity to produce IgA and IgG, which demonstrate significantly higher objective response rates and a superior prognosis for ccRCC patients subjected to immunotherapy. In conclusion, this study revealed the implications of TLS spatial and maturation heterogeneity on the immunological status and clinical responses, allowing the improvement of precise immunotherapies of ccRCC.

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Section: Discussionmentioning

confidence: 99%

Unveiling the impact of tertiary lymphoid structures on immunotherapeutic responses of clear cell renal cell carcinoma

Xu,

Lu,

Tian

et al. 2024

MedComm

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“…A third form of cell-cell interaction involved an intermembrane spacing of ~500 nm but only along a small area of the membrane (1-2 µm 2 ); such interactions could involve 100 or more immune cells, a pattern we have described previously as lymphocyte networks (lymphonets) 6 . We observed lymphonets comprised primarily of CD4 T cells or CD8 T cells, dendritic cells, and mixtures thereof (Fig.…”

Section: Membrane-membrane Interactionsmentioning

confidence: 97%

“…We then collected 11 datasets (Supplementary Table 2) from five tissue types (representing normal, cancerous, and pre-cancerous tissues) using thick section 3D high-plex CyCIF imaging with of tissue (see Supplementary Figs. [1][2][3][4][5][6][7][8][9][10][11] for illustrative results of all tissues). Antibody specificity was confirmed on 5 µm sections of the same specimens, as previously described 10 .…”

mentioning

confidence: 99%

Multiplexed 3D Analysis of Immune States and Niches in Human Tissue

Yapp,

Nirmal,

Zhou

et al. 2023

Preprint

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SUMMARYHomeostasis of normal tissues and the emergence of diseases such as cancer are controlled by changes in the proportions and states of diverse cell types, cell-cell interactions, and acellular components of the tissue microenvironment1. Spatial omics using highly multiplexed tissue profiling2makes it possible to study these processesin situ, usually on thin, 4-5 micron thick sections (the standard histopathology format)3. Microscopy-based tissue imaging is commonly performed at a resolution sufficient to determine cell types but not detect the subtle morphological features associated with cytoskeletal reorganisation, juxtracrine signalling, or membrane trafficking4. Here we introduce a 3D imaging approach using existing instruments and reagents that is able to characterize a wide variety of organelles and structures at sub-micron scale while simultaneously quantifying millimetre-scale spatial features. We perform high-resolution 54-plex cyclic immunofluorescence (CyCIF) imaging3on sections of primary human melanoma thick enough (30-40 microns) to fully encompass two or more layers of intact cells. In pre-invasive melanomain situ5, 3D imaging of entire cell volumes showed that transformed melanocytic cells are plastic in state and participate in tightly localised niches of interferon signalling near sites of initial invasion into the underlying dermis. Below this layer, immune cells engaged in an unexpectedly diverse array of membrane-membrane interactions as well as looser “neighbourhood” associations6. These data provide new insight into the transitions occurring during early tumour formation and demonstrate the potential for phenotyping tissues at a level of detail previously restricted to cultured cells and organoids.

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“…Further, the extraction of 3D cell morphological features such as sphericity (Fig. 6a, region 𝛼 ), enabled us to reveal looser "neighbourhood" associations 100 whose morphologies reveal functional states.…”

Section: U-segment3d Can Multiprocess To Generate Consensus 3d Segmen...mentioning

confidence: 99%

A general algorithm for consensus 3D cell segmentation from 2D segmented stacks

Zhou,

Yapp,

Shang

et al. 2024

Preprint

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Cell segmentation is the fundamental task. Only by segmenting, can we define the quantitative spatial unit for collecting measurements to draw biological conclusions. Deep learning has revolutionized 2D cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by the ease of scaling up image acquisition, annotation and computation. However 3D cell segmentation, which requires dense annotation of 2D slices still poses significant challenges. Labelling every cell in every 2D slice is prohibitive. Moreover it is ambiguous, necessitating cross-referencing with other orthoviews. Lastly, there is limited ability to unambiguously record and visualize 1000s of annotated cells. Here we develop a theory and toolbox, u-Segment3D for 2D-to-3D segmentation, compatible with any 2D segmentation method. Given optimal 2D segmentations, u-Segment3D generates the optimal 3D segmentation without data training, as demonstrated on 11 real life datasets, >70,000 cells, spanning single cells, cell aggregates and tissue.

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Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma

Cited by 34 publications

References 57 publications

Unveiling the impact of tertiary lymphoid structures on immunotherapeutic responses of clear cell renal cell carcinoma

Unveiling the impact of tertiary lymphoid structures on immunotherapeutic responses of clear cell renal cell carcinoma

Multiplexed 3D Analysis of Immune States and Niches in Human Tissue

A general algorithm for consensus 3D cell segmentation from 2D segmented stacks

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