Localization of T cell clonotypes using the Visium spatial transcriptomics platform

Hudson, William Henry; Sudmeier, Lisa

doi:10.1016/j.xpro.2022.101391

Cited by 36 publications

(34 citation statements)

References 8 publications

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“…Moreover, imaging tools can be leveraged to identify cells or tissue areas expressing specific surface markers that, coupled with gene expression, can guide downstream analyses [ 129 ]. Sudmeier and colleagues successfully paired spatial transcriptomics with TCR-seq by adapting the 10X Visium experimental protocol, revealing how tumor infiltrating CD8 + T-cell subpopulations have different phenotypes and localize in specific niches within brain metastasis tumor core or parenchyma, fostering the development of more specific immunotherapeutic strategies [ 42 , 130 ]. Recently, a novel methodology, referred as to Slide-TCR-seq, has been developed by Liu et al for sequencing whole transcriptomes and TCRs within intact tissue [ 42 , 43 , 130 ].…”

Section: Introductionmentioning

confidence: 99%

“…Sudmeier and colleagues successfully paired spatial transcriptomics with TCR-seq by adapting the 10X Visium experimental protocol, revealing how tumor infiltrating CD8 + T-cell subpopulations have different phenotypes and localize in specific niches within brain metastasis tumor core or parenchyma, fostering the development of more specific immunotherapeutic strategies [ 42 , 130 ]. Recently, a novel methodology, referred as to Slide-TCR-seq, has been developed by Liu et al for sequencing whole transcriptomes and TCRs within intact tissue [ 42 , 43 , 130 ]. The unprecedented opportunity to explore the TCR repertoire dynamics in their spatial context will be instrumental in understanding how the anatomical distribution of T cells and their partners shapes immune response, particularly within the dysregulated TME.…”

Section: Introductionmentioning

confidence: 99%

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T-cell repertoire diversity: friend or foe for protective antitumor response?

Porciello¹,

Franzese

D’Ambrosio³

et al. 2022

J Exp Clin Cancer Res

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Profiling the T-Cell Receptor (TCR) repertoire is establishing as a potent approach to investigate autologous and treatment-induced antitumor immune response. Technical and computational breakthroughs, including high throughput next-generation sequencing (NGS) approaches and spatial transcriptomics, are providing unprecedented insight into the mechanisms underlying antitumor immunity. A precise spatiotemporal variation of T-cell repertoire, which dynamically mirrors the functional state of the evolving host-cancer interaction, allows the tracking of the T-cell populations at play, and may identify the key cells responsible for tumor eradication, the evaluation of minimal residual disease and the identification of biomarkers of response to immunotherapy. In this review we will discuss the relationship between global metrics characterizing the TCR repertoire such as T-cell clonality and diversity and the resultant functional responses. In particular, we will explore how specific TCR repertoires in cancer patients can be predictive of prognosis or response to therapy and in particular how a given TCR re-arrangement, following immunotherapy, can predict a specific clinical outcome. Finally, we will examine current improvements in terms of T-cell sequencing, discussing advantages and challenges of current methodologies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

T-cell repertoire diversity: friend or foe for protective antitumor response?

Porciello¹,

Franzese

D’Ambrosio³

et al. 2022

J Exp Clin Cancer Res

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show abstract

“…Current methods to integrate spatial location and single-cell transcriptomics have difficulty additionally obtaining full-length immune receptor information for cells at high throughput. Despite ongoing work to generate experimental data sets that integrate cell location, transcriptome and immune receptor sequence, these datasets currently only contain heavy chain information for a few cells (e.g., roughly 27 T cells for an entire image) ( Hudson and Sudmeier, 2022 ). These challenges have thereby prevented the development of computational methods capable of integrating spatial information into clonal relationships.…”

Section: Resultsmentioning

confidence: 99%

Echidna: integrated simulations of single-cell immune receptor repertoires and transcriptomes

Han

Masserey

Shlesinger

et al. 2022

Bioinformatics Advances

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Motivation Single-cell sequencing now enables the recovery of full-length immune receptor repertoires [B cell receptor (BCR) and T cell receptor (TCR) repertoires], in addition to gene expression information. The feature-rich datasets produced from such experiments require extensive and diverse computational analyses, each of which can significantly influence the downstream immunological interpretations, such as clonal selection and expansion. Simulations produce validated standard datasets, where the underlying generative model can be precisely defined and furthermore perturbed to investigate specific questions of interest. Currently, there is no tool that can be used to simulate single-cell datasets incorporating immune receptor repertoires and gene expression. Results We developed Echidna, an R package that simulates immune receptors and transcriptomes at single-cell resolution with user-tunable parameters controlling a wide range of features such as clonal expansion, germline gene usage, somatic hypermutation, transcriptional phenotypes, and spatial location. Echidna can additionally simulate time-resolved B cell evolution, producing mutational networks with complex selection histories incorporating class-switching and B cell subtype information. We demonstrated the benchmarking potential of Echidna by simulating clonal lineages and comparing the known simulated networks with those inferred from only the BCR sequences as input. Finally, we simulated immune repertoire information onto existing spatial transcriptomic experiments, thereby generating novel datasets that could be used to develop and integrate methods to profile clonal selection in a spatially-resolved manner. Together, Echidna provides a framework that can incorporate experimental data to simulate single-cell immune repertoires to aid software development and bioinformatic benchmarking of clonotyping, phylogenetics, transcriptomics and machine learning strategies. Supplementary information Supplementary data are available at Bioinformatics Advances online.

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“…Despite these challenges this is the only platform, among those compared, that does not require the use of targeted RNA probes, thereby enabling capture of all intrinsic RNA molecules with poly-A sequences. This can be particularly valuable for profiling transcripts whose nucleotide sequences are variable, for example, TCR or BCR [39].…”

Section: Discussionmentioning

confidence: 99%

An experimental comparison of the Digital Spatial Profiling and Visium spatial transcriptomics technologies for cancer research

Wang

Harvey

Reeves

et al. 2023

Preprint

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Background: Spatial transcriptomic technologies are powerful tools for resolving the spatial heterogeneity of gene expression in tissue samples. However, little evidence exists on relative strengths and weaknesses of the various available technologies for profiling human tumour tissue. In this study, we aimed to provide an objective assessment of two common spatial transcriptomics platforms, 10X Genomics Visium and Nanostrings GeoMx DSP. Method: The abilities of the DSP and Visium platforms to profile transcriptomic features were compared using matching cell line and primary breast cancer tissue samples. A head-to-head comparison was conducted using data generated from matching samples and synthetic tissue references. Platform specific features were also assessed according to manufacturers recommendations to evaluate the optimal usage of the two technologies. Results: We identified substantial variations in assay design between the DSP and Visium assays such as transcriptomic coverage and composition of the transcripts detected. When the data was standardised according to manufacturers recommendations, the DSP platform was more sensitive in gene expression detection. However, its specificity was diminished by the presence of non-specific detection. Our results also confirmed the strength and weakness of each platform in characterising spatial transcriptomic features of tissue samples, in particular their application to hypothesis generation versus hypothesis testing. Conclusion: In this study, we share our experience on both DSP and Visium technologies as end users. We hope this can guide future users to choose the most suitable platform for their research. In addition, this dataset can be used as an important resource for the development of new analysis tools.

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Localization of T cell clonotypes using the Visium spatial transcriptomics platform

Cited by 36 publications

References 8 publications

T-cell repertoire diversity: friend or foe for protective antitumor response?

T-cell repertoire diversity: friend or foe for protective antitumor response?

Echidna: integrated simulations of single-cell immune receptor repertoires and transcriptomes

An experimental comparison of the Digital Spatial Profiling and Visium spatial transcriptomics technologies for cancer research

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