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  1. Response to Reviewer #1 (Public Review):

    [...] The authors conduct an analysis of eQTL per each cell type to get at specificity. This identifies a number of eQTL found in only a single cell type but these binary tests can have an ascertainment issue that may be over-estimating the cell type specificity. Optimally, this would be conducted by incorporating the different cell types as different environments within a single eQTL model but given the different sample sizes, this may not be feasible. Alternatively an investigation of how eQTLs specific to one cell type are or are not found by shifting the detection threshold in the other tissues could test this possibility.

    We agree with the reviewer’s important comment. The absence of eQTL replication is not, in itself, evidence of cell-type specificity. For this reason, we were cautious to avoid any claims about cell-type specificity except in the nervous system, where we carried out additional analyses (relaxed threshold and sign analysis), and identified direct evidence for antagonistic effects in the case of nlp-21.

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  2. Reviewer #3 (Public Review):

    The paper by Eyal Ben-David and colleagues reports an elegant single cell experiment in a genetic outcross of C. elegans to show where specific genetic regulation of gene expression could be seen at the level of individual cells. This is the first, to my knowledge, genetic mapping experiment at the single cell level in a complex organism. One neat trick was use the transcript sequencing data for genotyping each individual cell. Another above-and-beyond-the-call-of-duty feature was the permutation tests to set FDR levels, which ended up being similar to Benjamini-Hochberg.

    There is complex single cell processing to analyse this data. It could be more clear how complex this analysis is: quite complex models are used to both (a) cluster the cells into cell types across each individual and (b) model the resulting eQTLs. (c) somewhat more routinely, a HMM is used to gentoype but from the single cell transcript data, which is cute. Personally I think more should be made in the main text of the methods, highlighting the complexity of the models (there is at least one parameter this reviewer did not understand why was in the model!). However, a variety of bulk to single cell or single cell to previous experiment data shows that they seem to have discovered correct eQTLs.

    A particular focus was on single cell neuronal eQTLs; this plays to the unique "named cell" aspect of C. elegans and this dataset, and did not disappoint. they found a fair number and one that they highlighted had the (rare) antagonistic effect between cell lines, something much discussed or theorised might exist in some cell types - here it is in all its glory. Backing up this was evidence that the single cell neuronal QTL data cannot be seen by "pan neuronal" analysis.

    Overall this is an excellent paper; it clarifies much of which has been theorised or discussed, while in many ways (in my view) hiding its methodological sophistication in the main text.

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  3. Reviewer #2 (Public Review):

    eQTLs can vary between cell types. To capture this in an organism as complex as a mammal looks daunting and expensive if eQTLs have to be mapped a single cell type at a time. However, here the authors propose a 'one pot' method where whole animals are dissociated and the cell types deconvoluted based on a robust set of markers. Thus in a single experiment, eQTLS can be mapped in tens of cell types at once - here they identify 19 major cell types but in the case of the nervous system break it down with even more specificity, down to individual cells.

    They test their method in C. elegans which is ideal for this - the lineage is invariant, there are extensive sets of cell type specific markers, and they can exploit their previously published method called ceX-QTL to generate massive pools of segregants using an elegant genetic trick.

    Overall I was extremely impressed with the clarity of writing, the care of data analysis, and I honestly found that every analysis I was looking for had been done. They highlight some beautiful findings, most striking of which was the opposing regulation of nlp-21 in two neurons, a perfect example of the resolution this can achieve.

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  4. Reviewer #1 (Public Review):

    In this manuscript, the authors use single cell RNA sequencing to investigate cell-type specific eQTL within C. elegans. This relies on the well known ability to genotype individuals via their transcriptome allowing the authors to generate both phenotypes and genotypes from single cell transcriptomes. This identifies a blend of cis and trans-eQTL that are cell type specific and starts to provide numerical observations to the communities expectation of cell type specificity.

    The use of simultaneous single cell sequencing on a diversity of individuals is a unique method that is absolutely essential to get around the vast scale issues that are presented when contemplating single cell eQTL within multicellular organisms. However, an unfortunate outcome of this approach that the cell-autonomy of the eQTL cannot be studied. Instead the cell types have to be considered completely independent of each other.

    The authors conduct an analysis of eQTL per each cell type to get at specificity. This identifies a number of eQTL found in only a single cell type but these binary tests can have an ascertainment issue that may be over-estimating the cell type specificity. Optimally, this would be conducted by incorporating the different cell types as different environments within a single eQTL model but given the different sample sizes, this may not be feasible. Alternatively an investigation of how eQTLs specific to one cell type are or are not found by shifting the detection threshold in the other tissues could test this possibility.

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  5. Evaluation Summary:

    The authors use a pooled single cell sequencing approach to simultaneously genotype and phenotype C. elegans. This allows them to begin to query the genetic architecture of cell specific eQTLs in a multi-cellular organism.

    (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 #3 agreed to share their name with the authors.)

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