Biology subjects

Gersuk, V. H.

Publications and source records attributed to Gersuk, V. H..

3 recordsLinked to original sources

bioRxiv2021

A simple strategy for sample annotation error detection in cytometry datasets

Mislabeling samples or data with the wrong participant information can impact 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 HLA class I alleles. We measured HLA-A*02 and HLA-B*07 expression in 3 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.

immunology↗

bioRxiv2019

DCA promotes a novel tolerogenic program in CD4+ T cells by inhibiting CDK8

Immune health requires innate and adaptive immune cells to engage precisely balanced pro- and anti-inflammatory forces. We employ the concept of chemical immunophenotypes to classify small molecules functionally or mechanistically according to their patterns of effects on primary innate and adaptive immune cells. The high-specificity, low-toxicity cyclin dependent kinase 8 (CDK8) inhibitor DCA exerts a distinct tolerogenic profile in both innate and adaptive immune cells. DCA promotes Treg and Th2 differentiation, while inhibiting Th1 and Th17 differentiation, in both murine and human cells. This unique chemical immunophenotype led to mechanistic studies showing that DCA promotes Treg differentiation in part by regulating a previously undescribed CDK8-GATA3-FOXP3 pathway that regulates early pathways of Foxp3 expression. These results highlight previously unappreciated links between Treg and Th2 differentiation and extend our understanding of the transcription factors that regulate Treg differentiation and their temporal sequencing. These findings have significant implications for future mechanistic and translational studies of CDK8 and CDK8 inhibitors.

immunology↗

bioRxiv2019

A Transcriptome Fingerprinting Assay for Clinical Immune Monitoring

BackgroundWhile our understanding of the role that the immune system plays in health and disease is growing at a rapid pace, available clinical tools to capture this complexity are lagging. We previously described the construction of a third-generation modular transcriptional repertoire derived from genome-wide transcriptional profiling of blood of 985 subjects across 16 diverse immunologic conditions, which comprises 382 distinct modules.\n\nResultsHere we describe the use of this modular repertoire framework for the development of a targeted transcriptome fingerprinting assay (TFA). The first step consisted in down-selection of the number of modules to 32, on the basis of similarities in changes in transcript abundance and functional interpretation. Next down-selection took place at the level of each of the 32 modules, with each one of them being represented by four transcripts in the final 128 gene panel. The assay was implemented on both the Fluidigm high throughput microfluidics PCR platform and the Nanostring platform, with the list of assays target probes being provided for both. Finally, we provide evidence of the versatility of this assay to assess numerous immune functions in vivo by demonstrating applications in the context of disease activity assessment in systemic lupus erythematosus and longitudinal immune monitoring during pregnancy.\n\nConclusionsThis work demonstrates the utility of data-driven network analysis applied to large-scale transcriptional profiling to identify key markers of immune responses, which can be downscaled to a rapid, inexpensive, and highly versatile assay of global immune function applicable to diverse investigations of immunopathogenesis and biomarker discovery.

immunology↗