Background: A significant challenge in the field of allograft transplantation is the efficient evaluation of tissue-specific immune responses and infiltrating immune cell types.
Method: We have developed a new bioinformatics approach, called LineageProfiler, that couples the unbiased genomic analysis of transplantation gene expression profiles with a large panel of immune-cell sub-type specific marker profiles to identify infiltrating cell types. LineageProfiler works directly with unprocessed microarray and RNA-Seq data as well as a prior identified gene lists through the powerful open-source analysis packages AltAnalyze (http://www.altanalyze.org), and GO-Elite (http://genmapp.org/go_elite).
Results: We analyzed over 700 publicly available mouse and human immune cell expression arrays, corresponding to over 230 distinct cell subsets, to identify cell specific markers that could distinguish biopsy confirmed acute rejection (AR), chronic allograft injury (CAI) and BK virus nephropathy (BKVN). We applied the LineageProfiler marker sets to 168 renal allograft biopsy microarray expression profiles from transplanted kidneys with AR, BKVN, CAI and stable grafts. Analysis of differentially expressed genes between these clinical groups was able to identify distinct immune cell signatures separating out these different graft injury group. These results highlight distinct putative immune responses, which we have begun to successfully validate immunohistochemically, as well across independent renal allograft injury microarray datasets. Using our enrichment analysis tool GO-Elite, we have further identified candidate upstream transcriptional regulators and pathways signatures that distinguish these allograft injury paradigms.
Conclusion: LineageProfiler provides a highly sensitive and accurate means for evaluating cellular infiltration in host-graft responses, thus uncovering novel cellular mechanisms for specific types of graft injury. This approach allows for the creation of cell-specific transplant injury models that can be targeted for customized therapies to improve graft outcomes.
To cite this abstract in AMA style:Salomonis N, Naesens M, Sigdel T, Sarwal M. LineageProfiler: A Novel Informatics Tool for Unraveling Transplant Injury [abstract]. Am J Transplant. 2013; 13 (suppl 5). http://atcmeetingabstracts.com/abstract/lineageprofiler-a-novel-informatics-tool-for-unraveling-transplant-injury/. Accessed November 19, 2017.
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