Monk, E. | bioRxiv Science

bioRxiv2022

PhenoComb: A discovery tool to assess complex phenotypes in high-dimension, single-cell datasets

MotivationHigh-dimension cytometry assays can simultaneously measure dozens of markers, enabling the investigation of complex phenotypes. However, as manual gating relies on previous biological knowledge, few marker combinations are often assessed. This results in complex phenotypes with potential for biological relevance being overlooked. Here we present PhenoComb, an R package that allows agnostic exploration of phenotypes by assessing all combinations of markers. DesignPhenoComb uses signal intensity thresholds to assign markers to discrete states (e.g. negative, low, high) and then counts the number of cells per sample from all possible marker combinations in a memory-safe manner. Time and disk space are the only constraints on the number of markers evaluated. PhenoComb also provides several approaches to perform statistical comparisons, evaluate the relevance of phenotypes, and assess the independence of identified phenotypes. PhenoComb allows users to guide analysis by adjusting several function arguments such as identifying parent populations of interest, filtering of low-frequency populations, and defining a maximum complexity of phenotypes to evaluate. We have designed PhenoComb to be compatible with local computer or server-based use. ResultsIn testing of PhenoCombs performance on synthetic datasets, computation on 16 markers was completed in the scale of minutes and up to 26 markers in hours. We applied PhenoComb to two publicly available datasets: an HIV flow cytometry dataset (12 markers and 421 samples) and the COVIDome CyTOF dataset (40 markers and 99 samples). In the HIV dataset, PhenoComb identified immune phenotypes associated with HIV seroconversion, including those highlighted in the original publication. In the COVID dataset, we identified several immune phenotypes with altered frequencies in infected individuals relative to healthy individuals. Collectively, PhenoComb represents a powerful discovery tool for agnostically assessing high-dimension, single-cell data. AvailabilityThe PhenoComb R package can be downloaded from https://github.com/SciOmicsLab/PhenoComb

bioinformatics↗

bioRxiv2021

Simultaneous assessment of eight phosphorylated STAT residues in T-cells by flow cytometry.

Signal transducer and activator of transcription (STAT) proteins are a family of transcription factors controlling functions in immune responses and other cell types. Given their importance, we developed a flow cytometry panel to assess eight phosphorylated STAT residues in human T-cells, including six tyrosine residues across six STAT proteins (STAT1, STAT2, STAT3, STAT4, STAT5a, STAT6) and additional serine residues on STAT1 and STAT3. We applied this protocol to test the in vitro induction of pSTATs in response to CD3/CD28 activation and a panel of recombinant cytokines. We also assessed the pSTAT expression profiles of naive CD4+ T-cells polarized to Th1, Th2, Th17 or iTregs. pSTAT1(S727), pSTAT2(Y689) and pSTAT3(S727) were constitutively expressed in most T-cells, even in the absence of stimulation. For pSTAT1(S727) and pSTAT3(S727), we observed two positive states, high and low. Conversely, expression of pSTAT1(Y701), pSTAT3(Y705), pSTAT4(Y693) and pSTAT6(Y641) were absent in resting T-cells and only expressed with CD3/CD28 activation or with specific cytokines. Variable frequencies of pSTAT5a(Y694) expression were observed in resting T-cells, which increased with activation or specific cytokine stimulation (e.g. IL-2). IFN{beta} stimulation enhanced frequencies of expressing cells for all pSTATs. Correlations among several pSTATs, particularly pSTAT1(S727)high and pSTAT3(S727)high were observed. While polarization resulted in increases in canonically associated pSTATs, other non-canonical pSTAT changes were also observed. Collectively, we developed, optimized, and tested a sensitive and rapid approach for simultaneously assessing phosphorylation of six STAT proteins. Using this approach, we made several novel observations of T-cell pSTAT induction in response to stimuli.

immunology↗