Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease-specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders EXpanding (T-REX) was created to identify changes in both rare and common cells across human immune monitoring settings. T-REX identified cells with highly similar phenotypes that localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized MHCII tetramer reagents that mark rhinovirus-specific CD4+ cells were left out during analysis and then used to test whether T-REX identified biologically significant cells. T-REX identified rhinovirus-specific CD4+ T cells based on phenotypically homogeneous cells expanding by ≥95% following infection. T-REX successfully identified hotspots of virus-specific T cells by comparing infection (day 7) to either pre-infection (day 0) or post-infection (day 28) samples. Plotting the direction and degree of change for each individual donor provided a useful summary view and revealed patterns of immune system behavior across immune monitoring settings. For example, the magnitude and direction of change in some COVID-19 patients was comparable to blast crisis acute myeloid leukemia patients undergoing a complete response to chemotherapy. Other COVID-19 patients instead displayed an immune trajectory like that seen in rhinovirus infection or checkpoint inhibitor therapy for melanoma. The T-REX algorithm thus rapidly identifies and characterizes mechanistically significant cells and places emerging diseases into a systems immunology context for comparison to well-studied immune changes.

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders Expanding (T-REX) was created to identify changes in both very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized reagents used to detect the rhinovirus-specific CD4+ cells, MHCII tetramers, were not used during unsupervised analysis and instead 'left out' to serve as a test of whether T-REX identified biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4+ T cells based on these cells being a distinct phenotype that expanded by ≥95% following infection. T-REX successfully identified hotspots containing virus-specific T cells using pairs of samples comparing Day 7 of infection to samples taken either prior to infection (Day 0) or after clearing the infection (Day 28). Mapping pairwise comparisons in samples according to both the direction and degree of change provided a framework to compare systems level immune changes during infectious disease or therapy response. This revealed that the magnitude and direction of systemic immune change in some COVID-19 patients was comparable to that of blast crisis acute myeloid leukemia patients undergoing induction chemotherapy and characterized the identity of the immune cells that changed the most. Other COVID-19 patients instead matched an immune trajectory like that of individuals with rhinovirus infection or melanoma patients receiving checkpoint inhibitor therapy. T-REX analysis of paired blood samples provides an approach to rapidly identify and characterize mechanistically significant cells and to place emerging diseases into a systems immunology context.

Systems analysis of the transcriptional response of human ileocecal epithelial cells to Clostridium difficile toxins and effects on cell cycle control

Background

Toxins A and B (TcdA and TcdB) are Clostridium difficile's principal virulence factors, yet the pathways by which they lead to inflammation and severe diarrhea remain unclear. Also, the relative role of either toxin during infection and the differences in their effects across cell lines is still poorly understood. To better understand their effects in a susceptible cell line, we analyzed the transciptome-wide gene expression response of human ileocecal epithelial cells (HCT-8) after 2, 6, and 24 hr of toxin exposure.

Results

We show that toxins elicit very similar changes in the gene expression of HCT-8 cells, with the TcdB response occurring sooner. The high similarity suggests differences between toxins are due to events beyond transcription of a single cell-type and that their relative potencies during infection may depend on differential effects across cell types within the intestine. We next performed an enrichment analysis to determine biological functions associated with changes in transcription. Differentially expressed genes were associated with response to external stimuli and apoptotic mechanisms and, at 24 hr, were predominately associated with cell-cycle control and DNA replication. To validate our systems approach, we subsequently verified a novel G₁/S and known G₂/M cell-cycle block and increased apoptosis as predicted from our enrichment analysis.

Conclusions

This study shows a successful example of a workflow deriving novel biological insight from transcriptome-wide gene expression. Importantly, we do not find any significant difference between TcdA and TcdB besides potency or kinetics. The role of each toxin in the inhibition of cell growth and proliferation, an important function of cells in the intestinal epithelium, is characterized.

Optimization of yeast cell cycle analysis and morphological characterization by multispectral imaging flow cytometry

Budding yeast Saccharoymyces cerevisiae is a powerful model system for analyzing eukaryotic cell cycle regulation. Yeast cell cycle analysis is typically performed by visual analysis or flow cytometry, and both have limitations in the scope and accuracy of data obtained. This study demonstrates how multispectral imaging flow cytometry (MIFC) provides precise quantitation of cell cycle distribution and morphological phenotypes of yeast cells in flow. Cell cycle analysis of wild-type yeast, nap1Delta, and yeast overexpressing NAP1, was performed visually, by flow cytometry and by MIFC. Quantitative morphological analysis employed measurements of cellular length, thickness, and aspect ratio in an algorithm to calculate a novel feature, bud length. MIFC demonstrated reliable quantification of the yeast cell cycle compared to morphological and flow cytometric analyses. By employing this technique, we observed both the G2/M delay and elongated buds previously described in the nap1Delta strain. Using MIFC, we demonstrate that overexpression of NAP1 causes elongated buds yet only a minor disruption in the cell cycle. The different effects of NAP1 expression level on cell cycle and morphology suggests that these phenotypes are independent. Unlike conventional yeast flow cytometry, MIFC generates complete cell cycle profiles and concurrently offers multiple parameters for morphological analysis.

Lung eosinophils homing in homeostasis (98.2)

Severe lung eosinophilia is a significant feature of the inflammatory response in asthma. Besides producing various secretory products that mediate tissue injury and leukocyte activation, eosinophils from inflamed tissues have been shown to present antigens to T cells. However, the phenotype and function of eosinophils in normal tissues is poorly characterized and their antigen presentation capacity controversial. With magnetic microbead selection against the major eosinophil markers, eosinophils from normal lung tissues have been enriched to 50% of the selected cells. Surface marker staining showed that eosinophils in homeostasis expressed low to undetectable levels of MHC class II and CD11c, two markers expressed by antigen presenting cells. However, they expressed the costimulation molecules CD80 and CD86. Unactivated eosinophils expressed integrins α4, α5, and α6 chains of the β1 family, high levels of the αL (CD11a) and αM (CD11b) chains of the β2 family, and αvβ3 and α4β7. They also expressed high levels of other adhesion molecules including SiglacF, ICAM-1, ICAM-2, PSGL-1, CD24, and CD44. Besides CCR3, eosinophils expressed CCR2 and CCR2-deficient mice showed marked reduction in lung eosinophils. These data indicate that in homeostasis, eosinophil antigen presentation requires the upregulation of MHC class II, whereas costimulation molecules are already on the surface. During antigen-mediated allergic airway inflammation, MHC class II expression on eosinophils is induced, rendering the eosinophils presentation-competent. Besides CCR3, eosinophil homing to lungs is mediated by CCR2 and a wide array of adhesion molecules.