Single-cell transcriptomics applied to emigrating cells from psoriasis elucidate pathogenic versus regulatory immune cell subsets

Kim, Jaehwan; Lee, Jongmi; Kim, Hyun Je; Kameyama, Naoya; Nazarian, Roya; Der, Evan; Cohen, Steven R.; Guttman‐Yassky, Emma; Putterman, Chaim; Krueger, James G.

doi:10.1016/j.jaci.2021.04.021

Cited by 81 publications

(111 citation statements)

References 51 publications

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“…In conclusion, understanding skin fibroblast heterogeneity is of great relevance not only in skin homeostasis, but also in ageing 11,31 and disease. [32][33][34][35][36] We sincerely hope these reanalyses help further advance the field of single-cell transcriptomics of human skin. Further refinement of fibroblasts subsets and their identity-defining features will provide a fruitful framework for the advancement of knowledge as well as for the development of novel therapeutic approaches in dermatological disease and skin cancer.…”

Section: Discussionmentioning

confidence: 97%

The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing

2022

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Background: The advent of single-cell RNA sequencing (scRNAseq) and additional single-cell omics technologies have provided scientists with unprecedented tools to explore biology at cellular resolution. However, reaching an appropriate number of good quality reads per cell and reasonable numbers of cells within each of the populations of interest are key to infer relevant conclusions about the underlying biology of the dataset. For these reasons, scRNAseq studies are constantly increasing the number of cells analysed and the granularity of the resultant transcriptomics analyses. Methods: We aimed to identify previously described fibroblast subpopulations in healthy adult human skin by using the largest dataset published to date (528,253 sequenced cells) and an unsupervised population-matching algorithm. Results: Our reanalysis of this landmark resource demonstrates that a substantial proportion of cell transcriptomic signatures may be biased by cellular stress and response to hypoxic conditions. Conclusions: We postulate that careful design of experimental conditions is needed to avoid long processing times of biological samples. Additionally, computation of large datasets might undermine the extent of the analysis, possibly due to long processing times.

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Section: Discussionmentioning

confidence: 97%

The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing

2022

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“…In conclusion, understanding skin fibroblast heterogeneity is of great relevance not only in homeostasis, but also in ageing 11,29 and disease. [30][31][32][33][34] Further refinement of fibroblasts subsets and their identity-defining features will provide a fruitful framework for the advancement of knowledge as well as for the development of novel therapeutic approaches in dermatological disease and skin cancer.…”

Section: Discussionmentioning

confidence: 99%

The need to reassess single-cell RNA sequencing datasets: more is not always better

2021

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Background: The advent of single-cell RNA sequencing (scRNAseq) and additional single-cell omics technologies have provided scientists with unprecedented tools to explore biology at cellular resolution. However, reaching an appropriate number of good quality reads per cell and reasonable numbers of cells within each of the populations of interest are key to infer conclusions from otherwise limited analyses. For these reasons, scRNAseq studies are constantly increasing the number of cells analysed and the granularity of the resultant transcriptomics analyses. Methods: We aimed to identify previously described fibroblast subpopulations in healthy adult human skin by using the largest dataset published to date (528,253 sequenced cells) and an unsupervised population-matching algorithm. Results: Our reanalysis of this landmark resource demonstrates that a substantial proportion of cell transcriptomic signatures may be biased by cellular stress and response to hypoxic conditions. Conclusions: We postulate that the ”more is better” approach, currently prevalent in the scientific community, might undermine the extent of the analysis, possibly due to long computational processing times inherent to large datasets.

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“…Cheng et al [43] discovered a CD1C + CD301A + DC population not previously described, and Hughes et al [70] discovered an over-expression of CCL17, CCL22, and IL12B in an IRF4 + DC population. Gao et al and Kim et al use ligand-receptor analysis to show that DCs bind to T-cells, melanocytes, and suprabasal keratinocytes using LILRB1 and LILRB2 [80] and to pericytes, fibroblasts, and basal epidermis using IL36G, WNT5A, and CD58 [81]. Most of these cell types overexpressed either MHC-I or MHC-II [81].…”

Section: Psoriasismentioning

confidence: 99%

“…Gao et al and Kim et al use ligand-receptor analysis to show that DCs bind to T-cells, melanocytes, and suprabasal keratinocytes using LILRB1 and LILRB2 [80] and to pericytes, fibroblasts, and basal epidermis using IL36G, WNT5A, and CD58 [81]. Most of these cell types overexpressed either MHC-I or MHC-II [81].…”

Section: Psoriasismentioning

confidence: 99%

Challenges and Opportunities for the Translation of Single-Cell RNA Sequencing Technologies to Dermatology

Ascensión

Araúzo-Bravo

Izeta

2022

Life

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Skin is a complex and heterogeneous organ at the cellular level. This complexity is beginning to be understood through the application of single-cell genomics and computational tools. A large number of datasets that shed light on how the different human skin cell types interact in homeostasis—and what ceases to work in diverse dermatological diseases—have been generated and are publicly available. However, translation of these novel aspects to the clinic is lacking. This review aims to summarize the state-of-the-art of skin biology using single-cell technologies, with a special focus on skin pathologies and the translation of mechanistic findings to the clinic. The main implications of this review are to summarize the benefits and limitations of single-cell analysis and thus help translate the emerging insights from these novel techniques to the bedside.

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Single-cell transcriptomics applied to emigrating cells from psoriasis elucidate pathogenic versus regulatory immune cell subsets

Cited by 81 publications

References 51 publications

The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing

The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing

The need to reassess single-cell RNA sequencing datasets: more is not always better

Challenges and Opportunities for the Translation of Single-Cell RNA Sequencing Technologies to Dermatology

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