| Hypothesis generation: areas in vaccinology that lend themselves to investigation by scRNA-seq |
| (i) Cellular responses to various adjuvants and vaccines |
| (ii) Transcriptional and antigen-specific responses to adjuvants and vaccines |
| (iii) Site-specific immunity induced by vaccinations (for example, at mucosal surfaces following HIV vaccination, in the liver following liver-stage malaria vaccination, or in the lungs following tuberculosis vaccination [147]) |
| (a) Spatial transcriptomics, soon to provide resolution at the single-cell level, could also be used |
| (iv) Single-cell transcriptomic signatures associated with neutralising antibody responses |
| (v) The accordance between protective transcriptional signatures in vaccine human challenge studies in nonendemic and endemic countries |
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| Experimental design: exploit the technology and make the most of precious samples |
| (i) Assess the need for scRNA-seq as the primary experimental technique and contemplate whether the question can be answered by established techniques (e.g., ELISAs, ELISpots, and flow cytometry) |
| (ii) Consider using bulk RNA-seq as an adjunct to scRNA-seq (e.g., bulk RNA-seq on all samples, with scRNA-seq on a subset to allow computational deconvolution) |
| (iii) Tailor the particular type of scRNA-seq to the experimental question (e.g., will alternative splicing be of interest? Use full-length transcript profiling if so) |
| (iv) Longitudinal gene expression and TCR/BCR profiling to track antigen-specific clones |
| (v) Define heterogeneity of cellular response in protected individuals |
| (vi) Generate monoclonal antibodies from TCR/BCR sequences for rational vaccine redesign |
| (vii) Use scRNA-seq as “backbone” to other technologies (e.g., G&T seq/CITE-seq) |
| (viii) Combine scRNA-seq with flow cytometry/FACS and/or magnetically assisted cell separation to isolate rare or specific cell types [148] |
| (ix) Multiplex samples according to genotype to reduce sample preparation time, reagent and sequencing costs, and batch effects [149, 150] |
| (x) Preserve leftover cells either by sorting into a plate or by preserving in fixative for later use (for example, in the reanalysis of an interesting sample) |
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| Analytical considerations: bioinformatic requirements/suggestions |
| (i) Plan with, budget, and include bioinformaticians who are capable of working with scRNA-seq data from study conception onwards |
| (ii) Use freely available packages that are regularly maintained. See Table 1 in the review by Zeng and Dai [129] for a list of computational tools for scRNA-seq analyses |
| (iii) Upload data files to Gene Expression Omnibus [151] with as much metadata as possible (and not contravening human subject guidelines, if using human samples) as both processed and raw data |
| (iv) Consider submission of data to other relevant databases such as Human Cell Atlas and/or TCGA |
| (v) Consider analysing a similar published dataset with the experimental dataset to increase statistical power and/or to ensure a novel pipeline reproduces results in a previously published study |
| (vi) Make freely available the code which relates to bespoke analyses |
