Mass Cytometry of the Human Mucosal Immune System Identifies Tissue- and Disease-Associated Immune Subsets

Unen, Vincent van; Li, Na; Molendijk, Ilse; Temurhan, Mine; Höllt, Thomas; Jong, Andrea E. van der Meulen–de; Verspaget, Hein W.; Mearin, M. Luisa; Mulder, Chris J.; Bergen, Jeroen van; Lelieveldt, Boudewijn P. F.; Koning, Frits

doi:10.1016/j.immuni.2016.04.014

Cited by 115 publications

(120 citation statements)

References 25 publications

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“…Beyond that, its convolution module allows the analysis of millions of cells as shown here for the developing pallium. Further, we foresee a potential application of the convolution strategy in other large data types, such as single-cell mass cytometry data, where iCells could improve resolution and decrease computation time (van Unen et al 2016;Bendall et al 2014).…”

Section: Discussionmentioning

confidence: 99%

bigSCale: an analytical framework for big-scale single-cell data

et al. 2018

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Single-cell RNA sequencing (scRNA-seq) has significantly deepened our insights into complex tissues, with the latest techniques capable of processing tens of thousands of cells simultaneously. Analyzing increasing numbers of cells, however, generates extremely large data sets, extending processing time and challenging computing resources. Current scRNA-seq analysis tools are not designed to interrogate large data sets and often lack sensitivity to identify marker genes. With bigSCale, we provide a scalable analytical framework to analyze millions of cells, which addresses the challenges associated with large data sets. To handle the noise and sparsity of scRNA-seq data, bigSCale uses large sample sizes to estimate an accurate numerical model of noise. The framework further includes modules for differential expression analysis, cell clustering, and marker identification. A directed convolution strategy allows processing of extremely large data sets, while preserving transcript information from individual cells. We evaluated the performance of bigSCale using both a biological model of aberrant gene expression in patient-derived neuronal progenitor cells and simulated data sets, which underlines the speed and accuracy in differential expression analysis. To test its applicability for large data sets, we applied bigSCale to assess 1.3 million cells from the mouse developing forebrain. Its directed down-sampling strategy accumulates information from single cells into index cell transcriptomes, thereby defining cellular clusters with improved resolution. Accordingly, index cell clusters identified rare populations, such as reelin (Reln)-positive Cajal-Retzius neurons, for which we report previously unrecognized heterogeneity associated with distinct differentiation stages, spatial organization, and cellular function. Together, bigSCale presents a solution to address future challenges of large single-cell data sets.[Supplemental material is available for this article.]Single-cell RNA sequencing (scRNA-seq) is at the forefront of techniques to chart molecular properties of individual cells. Recent microfluidic-based methods are scalable to tens of thousands of cells, enabling an unbiased sampling and in-depth characterization without prior knowledge Macosko et al. 2015;Zheng et al. 2017). Consequently, studies are less confined by the number of cells and aim to produce comprehensive cellular atlases of entire tissues, organs, and organisms (Regev et al. 2017). Increasing cell numbers, however, generate extremely large data sets, which extend processing time and challenge computing resources. Current scRNA-seq analysis tools are not designed to analyze data sets larger than thousands of cells and often lack sensitivity and specificity to identify marker genes for cell populations or experimental conditions.To address the challenges of large scRNA-seq data sets, we developed bigSCale, an analytical framework for the sensitive detection of population markers and differentially expressed genes, being scalable to analyze mil...

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Section: Discussionmentioning

confidence: 99%

bigSCale: an analytical framework for big-scale single-cell data

et al. 2018

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“…Similarly, post‐transcriptional modifications (ie, phosphorylation, acetylation, or glycosylation) leading to changes on functional activities of proteins cannot be detected using RT‐PCR. These considerations imply that new techniques such as single‐cell RNA sequencing or mass cytometry of individual cells may be more appropriate to actually demonstrate changes in the activation status of immune cells and particularly to identify the cell populations involved in the pathogenesis of IBS.…”

Section: Discussionmentioning

confidence: 99%

Expression of immune‐related genes in rectum and colon descendens of Irritable Bowel Syndrome patients is unrelated to clinical symptoms

Aguilera‐Lizarraga

Florens

Brussel

et al. 2019

Neurogastroenterology Motil

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Background Mucosal immune activation has been postulated to play an important role in the pathogenesis of irritable bowel syndrome (IBS). However, data are conflicting and often based on small patient cohorts. Here, we aimed to evaluate the gene expression of a large set of immune‐related genes in mucosal biopsies from IBS patients and healthy volunteers (HV). Methods A total of 171 IBS patients and 127 HV were included in the study. Rectum biopsies were collected from a cohort of 70 HV and 77 IBS patients (Rome III) and colon descendens biopsies from another cohort of 57 HV and 94 IBS patients (Rome II). Gene expression was assessed using OpenArray technology, and validated questionnaires were used to evaluate clinical characteristics (GI symptoms, somatization, anxiety, and depression). Key Results A subset of IBS patients (33%) with increased immune activation in the colon descendens was identified using multivariate analysis and displayed increased gene expression of IL1B (3‐fold change), prostaglandin synthase PTGS2 (2.1‐fold change), and the G‐protein‐coupled receptor MRGPRX2 (10.7‐fold change). Clinical characteristics in this subgroup were however similar to the rest of the patient cohort. Analysis of rectal biopsies failed to identify such subgroup of “immuno‐active” IBS patients in the other patient cohort. Conclusion A subset of IBS patients reveals evidence of immune activation in the colon descendens, but not in the rectum; however, gene expression is unrelated to clinical symptoms. To what extent this subgroup might however respond to anti‐inflammatory therapy remains to be investigated.

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“…Efforts are currently ongoing to harmonize individual mass cytometers’ performance to a common standard of signal intensities and detection limits [138]. Mass cytometry has been recently used to dissect the human mucosal immune system in health and disease, allowing the identification of 142 immune subsets with tissue and disease specificity [139]. This technology is expected to impact immune monitoring strategies and to accelerate the development of individualized therapeutics.…”

Section: Immune Monitoring Assaysmentioning

confidence: 99%

Systematic evaluation of immune regulation and modulation

Stroncek

Butterfield

Cannarile

et al. 2017

j. immunotherapy cancer

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Cancer immunotherapies are showing promising clinical results in a variety of malignancies. Monitoring the immune as well as the tumor response following these therapies has led to significant advancements in the field. Moreover, the identification and assessment of both predictive and prognostic biomarkers has become a key component to advancing these therapies. Thus, it is critical to develop systematic approaches to monitor the immune response and to interpret the data obtained from these assays. In order to address these issues and make recommendations to the field, the Society for Immunotherapy of Cancer reconvened the Immune Biomarkers Task Force. As a part of this Task Force, Working Group 3 (WG3) consisting of multidisciplinary experts from industry, academia, and government focused on the systematic assessment of immune regulation and modulation. In this review, the tumor microenvironment, microbiome, bone marrow, and adoptively transferred T cells will be used as examples to discuss the type and timing of sample collection. In addition, potential types of measurements, assays, and analyses will be discussed for each sample. Specifically, these recommendations will focus on the unique collection and assay requirements for the analysis of various samples as well as the high-throughput assays to evaluate potential biomarkers.

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Mass Cytometry of the Human Mucosal Immune System Identifies Tissue- and Disease-Associated Immune Subsets

Cited by 115 publications

References 25 publications

bigSCale: an analytical framework for big-scale single-cell data

bigSCale: an analytical framework for big-scale single-cell data

Expression of immune‐related genes in rectum and colon descendens of Irritable Bowel Syndrome patients is unrelated to clinical symptoms

Systematic evaluation of immune regulation and modulation

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