Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA

Zhang, Weiruo; Li, Irene; Reticker-Flynn, Nathan E.; Good, Zinaida; Chang, Serena; Samusik, Nikolay; Saumyaa, Saumyaa; Li, Yuanyuan; Zhou, Xin; Liang, Ruyi; Kong, Christina S.; Le, Quynh‐Thu; Gentles, Andrew J.; Sunwoo, John B.; Nolan, Garry P.; Engleman, Edgar G.; Plevritis, Sylvia K.

doi:10.1038/s41592-022-01498-z

Cited by 67 publications

(63 citation statements)

References 49 publications

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“…More importantly, current computational approaches represent a bottleneck, as significant manual operation is needed to annotate and validate cell types, which is particularly relevant when handling large tissue areas with millions of cells. Further improvements of computational approaches will enable a broader application of highly multiplexed immunofluorescence across large clinical cohorts [68][69][70].…”

Section: Discussionmentioning

confidence: 99%

Multimodal and spatially resolved profiling identifies distinct patterns of T-cell infiltration in nodal B-cell lymphoma entities

Roider

Baertsch

Fitzgerald

et al. 2022

Preprint

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T-cell-engaging immunotherapies have improved the treatment of nodal B-cell lymphoma, but responses vary highly. Future improvements of such therapies require better understanding of the variety of lymphoma-infiltrating T-cells. We employed single-cell RNA and T-cell receptor sequencing alongside quantification of surface proteins, flow cytometry and multiplexed immunofluorescence on 101 lymph nodes from healthy controls, and patients with diffuse large B-cell, mantle cell, follicular, or marginal zone lymphoma. This multimodal resource revealed entity-specific quantitative and spatial aberrations of the T-cell microenvironment. Clonal PD1+TCF7-but not PD1+TCF7+cytotoxic T-cells converged into terminally exhausted T-cells, the proportions of which were variable across entities and linked to inferior prognosis. In follicular and marginal zone lymphoma, we observed expansion of follicular helper and IKZF3+regulatory T-cells, which were clonally related and inversely associated with tumor grading. Overall, we portray lymphoma-infiltrating T-cells with unprecedented comprehensiveness and decipher both beneficial and adverse dimensions of T-cell response.

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Section: Discussionmentioning

confidence: 99%

Multimodal and spatially resolved profiling identifies distinct patterns of T-cell infiltration in nodal B-cell lymphoma entities

Roider

Baertsch

Fitzgerald

et al. 2022

Preprint

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“…Our tool has a great value in transferring annotations across levels of granularity to a new biological context and discovering novel biological states that have not been characterized in previous experiments. The annotation transfer methodology in STELLAR distinguishes it from previous spatial annotation tools that rely on predefined marker genes that define cell types [16].…”

Section: Discussionmentioning

confidence: 99%

“…While CELESTA tool has successfully avoided post hoc cluster reannotation for multiplexed in situ images [16], it is reliant upon the prior human supervision provided in the form of the set of marker genes of expected cell types which is often challenging to define for a new dataset. As new spatial datasets are being generated [17][18][19][20][21], there is a necessity for computational methods that simultaneously leverage molecular features and additional spatial context of cells while at the same time minimize manual human annotation effort.…”

Section: Introductionmentioning

confidence: 99%

Annotation of Spatially Resolved Single-cell Data with STELLAR

Brbić

Cao

Hickey

et al. 2021

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Spatial protein and RNA imaging technologies have been gaining rapid attention but current computational methods for annotating cells are based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method that utilizes spatial and molecular cell information to automatically assign cell types from an annotated reference set as well as discover new cell types and cell states. STELLAR transfers annotations across different dissection regions, tissues, and donors and detects higher-order tissue structures with dramatic time savings.

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“…The morphology metrics cell size, solidity and eccentricity were z-scored and used as initial quality control steps to remove segmentation artefacts. Cell phenotyping was performed using CELESTA on each with a custom generated cell expression matrix 57 57 . The phenotype assignments were qualitatively validated by plotting them in their original spatial location and comparing the patterns to the images.…”

Section: Methodsmentioning

confidence: 99%

A cellular and spatial map of salivary glands at single cell resolution reveals the functional basis of tertiary lymphoid structure formation in Sjogren’s syndrome

Nayar

Turner

Asam

et al. 2022

Preprint

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The key role of tertiary lymphoid structures in autoimmune and non-autoimmune conditions has been recently appreciated. While many of the molecular mechanisms involved in tertiary lymphoid structure (TLS) formation have been identified, their cellular sources and their temporal and spatial relationship to each other during the development of TLS remain unknown. Here we have constructed a cellular and functional map of key components involved in the formation of TLS in the minor salivary glands (SG) in humans. We have confirmed the presence of an immunofibroblast cell state and identified an undescribed immunopericyte cell state with potential immunological functions within TLS. The identification of TLS cellular and functional properties and their relevant modulators provided by this analysis provides key therapeutic cues for TLS associated conditions in autoimmunity and cancer.

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Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA

Cited by 67 publications

References 49 publications

Multimodal and spatially resolved profiling identifies distinct patterns of T-cell infiltration in nodal B-cell lymphoma entities

Multimodal and spatially resolved profiling identifies distinct patterns of T-cell infiltration in nodal B-cell lymphoma entities

Annotation of Spatially Resolved Single-cell Data with STELLAR

A cellular and spatial map of salivary glands at single cell resolution reveals the functional basis of tertiary lymphoid structure formation in Sjogren’s syndrome

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