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  1. Evaluation Summary:

    Kasper et al. have analyzed intra-ocular cells in four subjects with HLA-B27-associated acute anterior uveitis, 2 subjects with HLA-B27-negative anterior uveitis, and one subject with bacterial endophthalmitis using several assay techniques including single cell RNA-Seq, fluorescence activated cell sorting, and quantification of multiple cytokines. They discovered a unique pattern in HLA-B27 positive uveitis that exclusively featured plasmacytoid and classical dendritic cells (cDC) infiltrate and plasma cells. These might provide hints for the pathogenesis of this disease.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

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  2. Reviewer #1 (Public Review):

    This is an interesting, but small study of seven ocular fluid samples examined by scRNA-seq analysis from patients with non-infectious uveitis and one sample with infectious uveitis. The authors aimed to characterize the leukocyte composition of these samples and aimed to validate their findings by multicolour flow cytometry. They further analysed the levels of cytokines by multiplex immune assay. The study confirms previous data on the dominance of lymphocytes as infiltrates in ocular fluid samples and identified the major leukocyte lineages in the samples. The major strength is the unbiased cell population identification which is the power of single cell sequencing. Despite this strength, the small samples size, variable entities studied, and substantial variability in composition between the samples - which is intrinsic to clinical samples also noted by the authors - makes the impact of the work on the field not entirely certain. Another weakness is that the 'validation' by flow cytometry work is not based on hall mark genes for the clusters identified by scRNAseq and the proportions of cell types identified by scRNAseq and flow cytometry are not comparable. The authors achieved the unbiased characterization of samples of ocular fluid, but did not achieve in linking this information with the cytometry and cytokine data.

    It is uncertain how the cytometry and multiplex immunoassay complement the single cell work since the flow cytometry panel was based on common markers for leukocyte populations and not necessary based on key marker genes identified by scRNA-seq. For example, the marker genes shown in figure 1C are not used in flow cytometry and I believe this neutralizes the unbiased strategy by scRNAseq in the beginning of the work, which was a strong strategy.

    In the flow cytometry proportions, the B27 samples contain almost entirely granulocytes while this leukocyte population makes up {plus minus}25% in the scRNAseq data. Also, granulocytes proportions in B27-negative sample 1 and B27-positive sample look similar in scRNA-seq, while in flow cytometry the difference is nearly 6-fold. Although this could understandably be due to inter-assay/platform variability, but this would also point towards the the uncertainty in the differences between the groups as a whole. Especially, given the large inter-sample variablity in the scRNAseq. This makes the conclusions on group differences not very robust.

    The authors state they use the multiplex assay as a complement to transcriptomics and that this was predefined. Hovwer, based on the cell-cell interaction network it would be logical to go for cytokines such as TNFSF13B, TGFB1, CXCL9, but these are missing the the cytokine analysis. Again, the link between the proposed strategies is is a missed opportunity to really connect information.

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  3. Reviewer #2 (Public Review):

    When asked why he robbed banks, Willie Sutton is said to have replied, "Because that's where the money is." Too often researchers who study uveitis (intra-ocular inflammation) have been satisfied to look at blood or other tissue due to limited access to the eye. Kasper and colleagues have followed "Sutton's Law" to provide a comprehensive characterization of intra-ocular leukocytes in four subjects with HLA-B27-associated acute anterior uveitis, two subjects with HLA-B27 negative anterior uveitis, and one subject with bacterial endophthalmitis. Their techniques included single cell RNA Seq, multicolor fluorescence activated cell sorting, Luminex measurement of multiple cytokines in aqueous humor, measurements of cytokines in blood, software to suggest potential cell to cell interactions, and extrapolations from genome wide association studies to determine how genes identified in these studies might be influencing transcripts for cytokines within the eye. The result is an overwhelming wealth of data which is both tantalizing because of the multitude of clues to pathogenesis which have been discovered and slightly unsatisfying because of the small number of subjects involved. Perhaps the main conclusion is that dendritic cells seem especially abundant in the anterior chamber of those with HLA-B27-associated anterior uveitis.

    In this study, the institutional review board allowed only 11 subjects due to the invasive nature of obtaining cells from the eye. Due to technical reasons, only 7 of the ocular samples could be studied by single cell RNA-Seq. As the authors recognize, multiple factors could influence the results in addition to the diagnosis. These parameters include age, sex, disease duration, local medication, systemic medication, and co-morbidities. The challenge is further complicated because HLA-B27 negative anterior uveitis is undoubtedly a collection of several diseases. It is impossible to do valid statistical comparisons on the basis of only two controls with uveitis. The validity of the comparisons weakens further because controlling for potentially important variables is also impossible. Nonetheless, this group from Muenster, Germany, has produced a pioneering study in an extremely comprehensive manner. It should serve as a roadmap for further studies to confirm or refute these preliminary findings. Ultimately the challenge will be to devise therapies based on the insights that derive from this type of big data approach.

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