Yale University’s New Data Analysis Tool Finds That COVID-19 Patients Who Die Show CD16hiCD66blo Neutrophil And IFN-Gamma Granzyme B+ Th17 Cell Responses!
A new data analysis tool developed by researchers from Yale University called Multiscale PHATE has found that COVID-19 patients who die typically show CD16hiCD66blo neutrophil and IFN-Gamma Granzyme B+ Th17 cell responses!
While the biomedical community produces datasets that are increasingly complex and high dimensional, there is a need for more sophisticated computational tools to extract biological insights, especially when dealing with the COVID-19 disease.
The Yale researchers developed Multiscale PHATE, a method that sweeps through all levels of data granularity to learn abstracted biological features directly predictive of disease outcome. Built on a coarse-graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse resolutions for high-level summarizations of data and at fine resolutions for detailed representations of subsets.
The team applied Multiscale PHATE to a coronavirus disease 2019 (COVID-19) dataset with 54 million cells from 168 hospitalized patients and found that patients who die typically show CD16hiCD66blo neutrophil and IFN-γ+ granzyme B+ Th17 cell responses.
The study findings also showed that population groupings from Multiscale PHATE directly fed into a classifier predict disease outcome more accurately than naive featurizations of the data. Multiscale PHATE is broadly generalizable to different data types, including flow cytometry, single-cell RNA sequencing (scRNA-seq), single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), and clinical variables.
The new data analysis tool when combined with manifold density estimation (MELD)12, can identify cellular populations associated with patient outcome across resolutions.
At coarse resolutions, the study team identified T cells to be broadly protective, whereas monocytes and granulocytes are pathogenic. At finer resolution, the study findings identified CD16hiCD66b-neutrophil, CD14−CD16hiHLA-DRlo monocytes, and interferon-γ (IFN-γ)+ granzyme B+ T helper type 17 (Th17) cells to be associated with patient mortality.
Although coarse grain analysis reveals that a cell type (e.g., T cells) may be broadly protective, fine-grain analysis reveals that cellular subsets can be pathogenic, highlighting the need for a multiresolution approach.
The tsudy team showed that these Multiscale PHATE-derived cellular groupings can be used to predict outcome better than immunologist-curated populations and groupings produced by other graph-based clustering approaches.
The key findings from the study were:
-CD14−CD16hiHLA-Drlo monocytes were associated with COVID-19 mortality
-Plasmablast populations were associated with COVID-19 mortality
-Fine-grained analysis identified pathogenic Th17 cells
-Hyperactivated CD8+ TEMRA cells were associated with COVID-19 mortality
The study findings were published in the peer reviewed journal: Nature Biotechnology. https://www.nature.com/articles/s41587-021-01186-x
This new data analysis tool revealed the specific immune cell types associated with increased risk of de
ath from COVID-19.
a, Multiscale PHATE visualization of PBMCs identifies all major cell types based on cell type-specific markers. Colors denote cell type and size of a dot is proportional to number of cells represented. b, Visualization of mortality likelihood score computed by MELD on coarse-grain Multiscale PHATE visualization of PBMCs as visualized in a. c, Visualization of mortality likelihood score computed by MELD organized by cell type revealed enrichment of granulocytes, monocytes and B cells in patients who died of COVID-19. Each dot represents a grouping of cells at the resolution visualized in a. d, Zoom in of granulocyte population identified subsets of neutrophils and eosinophils based on expression of known markers. e, Visualization of mortality likelihood score in granulocyte population identified CD16hi neutrophils enriched in patients with worse outcomes. Key associations between markers and mortality likelihood scores in neutrophils computed by DREMI and visualized with DREVI.
Typically, immune system cells such as T cells and antibody-producing B cells are known to provide broad protection against pathogens such as SARS-CoV-2, the virus that causes COVID-19.
Large-scale data analyses of millions of cells have given scientists a broad overview of the immune system response to this particular virus. However, they also found that some immune cell responses including by cell types that are usually protective can occasionally trigger deadly inflammation and death in patients.
Although other data analysis tools that allow for examination down to the level of single cells have given scientists some clues about culprits in severe COVID cases, such focused views often lack the context of particular cell groupings that might cause better or poorer outcomes.
The new data analysis tool by Yale: Multiscale PHATE, which is a machine learning tool, allows researchers to pass through all resolutions of data, from millions of cells to a single cell, within minutes.
The new technology builds on an algorithm called PHATE, created in the lab of Dr Smita Krishnaswamy, an associate professor of genetics and computer science, at Yale, overcomes many of the shortcomings of existing data visualization tools.
Dr Manik Kuchroo, a doctoral candidate at Yale School of Medicine who helped develop the technology and is co-lead author of the paper told Thailand Medical News
, “Machine learning algorithms typically focus on a single resolution view of the data, ignoring information that can be found in other more focused views. For this reason, we created Multiscale PHATE which allows users to zoom in and focus on specific subsets of their data to perform more detailed analysis.”
Dr Kuchroo, who works in Krishnaswamy’s lab, used the new tool to analyze 55 million blood cells taken from 163 patients admitted to Yale New Haven Hospital with severe cases of COVID-19.
Looking broadly, they found that high levels T cells seem to be protective against poor outcomes while high levels of two white blood cell types known as granulocytes and monocytes were associated with higher levels of mortality.
But when the study team drilled down to a more granular level they surprisingly discovered that TH17, a helper T cell, was also associated with higher mortality when clustered with the immune system cells IL-17 and IFNG.
The study team said that by measuring quantities of these cells in the blood, they could predict whether the patient lived or died with 83% accuracy.
Dr Krishnaswamy said, “We were able to rank order risk factors of mortality to show which are the most dangerous.”
Most importantly, the new data analytical tool could be used to fine tune risk assessment in a host of diseases.
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