RNASeq_similarity_matrix: visually identify sample mix-ups in RNASeq data using a 'genomic' sequence similarity matrix

A simple strategy for sample annotation error detection in cytometry datasets

Mislabeling samples or data with the wrong participant information can affect study integrity and lead investigators to draw inaccurate conclusions. Quality control to prevent these types of errors is commonly embedded into the analysis of genomic datasets, but a similar identification strategy is not standard for cytometric data. Here, we present a method for detecting sample identification errors in cytometric data using expression of human leukocyte antigen (HLA) class I alleles. We measured HLA-A*02 and HLA-B*07 expression in three longitudinal samples from 41 participants using a 33-marker CyTOF panel designed to identify major immune cell types. 3/123 samples (2.4%) showed HLA allele expression that did not match their longitudinal pairs. Furthermore, these same three samples' cytometric signature did not match qPCR HLA class I allele data, suggesting that they were accurately identified as mismatches. We conclude that this technique is useful for detecting sample-labeling errors in cytometric analyses of longitudinal data. This technique could also be used in conjunction with another method, like GWAS or PCR, to detect errors in cross-sectional data. We suggest widespread adoption of this or similar techniques will improve the quality of clinical studies that utilize cytometry.

Comparative transcriptome analysis of endemic and epidemic Kaposi's sarcoma (KS) lesions and the secondary role of HIV-1 in KS pathogenesis

In sub-Saharan Africa, endemic Kaposi's sarcoma (EnKS) is still prevalent despite high incidence of epidemic Kaposi's sarcoma (EpKS) resulting from the on-going HIV-1 epidemic. While KSHV is clearly the etiologic agent of KS, the mechanisms underlying KS development are not fully understood. For example, HIV-1 co-infection and concomitant immune dysfunction have been associated with EpKS development. However, the direct or indirect role(s) of HIV-1, and therefore of immune suppression, in EpKS remains unclear. How, or whether, EpKS is mechanistically distinct from EnKS is unknown. Thus, the absence of HIV-1 co-infection in EnKS provides a unique control for investigating and deciphering whether HIV-1 plays a direct or indirect role in the EpKS tumor microenvironment. We hypothesized that HIV-1 co-infection would induce transcriptome changes that differentiate EpKS from EnKS, thereby defining the direct intra-tumor role of HIV-1 in KS. Comparison of ART-treated and -naïve patients would further define the impact of ART on the KS transcriptome. We utilized RNA-seq followed by multiparameter bioinformatics analysis to compare transcriptomes from KS lesions to uninvolved control skin. We provide the first transcriptomic comparison of EpKS versus EnKS, ART-treated vs-naïve EpKS and male vs female EpKS to define the roles of HIV-1 co-infection, the impact of ART, and gender on KS gene expression profiles. Our findings suggest that ART-use and gender have minimal impact on transcriptome profiles of KS lesions. Gene expression profiles strongly correlated between EpKS and EnKS patients (Spearman r = 0.83, p<10-10). A subset of genes involved in tumorigenesis and inflammation/immune responses showed higher magnitude, but not unique dysregulation in EnKS compared to EpKS. While gender and ART had no detectable contribution, the trend toward higher magnitude of gene dysregulation in EnKS coupled with the absence of HIV-1 transcripts in EpKS may suggest an indirect or systemic effect of HIV-1 to promote KS tumorigenesis.