Multiplexed activity metabolomics for isolation of filipin macrolides from a hypogean actinomycete.

Chemical and biological stimulus screening in a hypogean actinomycete was used to elicit secondary metabolism. Optimal biosynthesis of bioactive natural products was identified using Multiplexed Activity Profiling for determining dose-dependent activity via six single-cell biological readouts. Bioactive extracts were fractioned to establish candidate compounds for isolation using Multiplexed Activity Metabolomics by correlating microtiter well-isolated phenotypes and extracted ion current peaks.

Defects in NK cell immunity of pediatric cancer patients revealed by deep immune profiling.

Systemic immunity plays an important role in cancer immune surveillance and response to therapy, but little is known about the immune status of children with solid cancers. We performed a high-dimensional single-cell analysis of systemic immunity in 50 treatment-naive pediatric cancer patients, comparing them to age-matched healthy children. Children with cancer had a lower frequency of peripheral NK cells, which was not due to tumor sequestration, had lower surface levels of activating receptors and increased levels of the inhibitory NKG2A receptor.

Single-cell profiling of the antigen-specific response to BNT162b2 SARS-CoV-2 RNA vaccine.

RNA-based vaccines against SARS-CoV-2 have proven critical to limiting COVID-19 disease severity and spread. Cellular mechanisms driving antigen-specific responses to these vaccines, however, remain uncertain. Here we identify and characterize antigen-specific cells and antibody responses to the RNA vaccine BNT162b2 using multiple single-cell technologies for in depth analysis of longitudinal samples from a cohort of healthy participants.

Two New Neutrophil Subsets Define a Discriminating Sepsis Signature.

Sepsis is the leading cause of death in adult ICUs. At present, sepsis diagnosis relies on nonspecific clinical features. It could transform clinical care to have immune-cell biomarkers that could predict sepsis diagnosis and guide treatment.

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.

Single-Cell Profiling of the Antigen-Specific Response to BNT162b2 SARS-CoV-2 RNA Vaccine.

Unlabelled: RNA-based vaccines against SARS-CoV-2 are critical to limiting COVID-19 severity and spread. Cellular mechanisms driving antigen-specific responses to these vaccines, however, remain uncertain. We used single-cell technologies to identify and characterized antigen-specific cells and antibody responses to the RNA vaccine BNT162b2 in longitudinal samples from a cohort of healthy donors.

CMV exposure drives long-term CD57+ CD4 memory T-cell inflation following allogeneic stem cell transplant.

Donor and recipient cytomegalovirus (CMV) serostatus correlate with transplant-related mortality that is associated with reduced survival following allogeneic stem cell transplant (SCT). Prior epidemiologic studies have suggested that CMV seronegative recipients (R-) receiving a CMV-seropositive graft (D+) experience inferior outcomes compared with other serostatus combinations, an observation that appears independent of viral reactivation. We therefore investigated the hypothesis that prior donor CMV exposure irreversibly modifies immunologic function after SCT.

DebarcodeR increases fluorescent cell barcoding capacity and accuracy.

Fluorescent cell barcoding (FCB) enables efficient collection of tens to hundreds of flow cytometry samples by covalently marking cells with varying concentration of spectrally distinct dyes. A key consideration in FCB is to balance the density of dye barcodes, the complexity of cells in the sample, and the desired accuracy of the debarcoding. Unfortunately, barcoding bench and computational methods have not benefited from the high dimensional revolution in cytometry due to a lack of automated computational tools that effectively balance these common cytometry needs.

Targeting In Vivo Metabolic Vulnerabilities of Th2 and Th17 Cells Reduces Airway Inflammation.

T effector cells promote inflammation in asthmatic patients, and both Th2 and Th17 CD4 T cells have been implicated in severe forms of the disease. The metabolic phenotypes and dependencies of these cells, however, remain poorly understood in the regulation of airway inflammation. In this study, we show the bronchoalveolar lavage fluid of asthmatic patients had markers of elevated glucose and glutamine metabolism.

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.

Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells.

A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to generate new therapeutic and biomarker hypotheses. We introduce a machine learning algorithm, Risk Assessment Population IDentification (RAPID), that is unsupervised and automated, identifies phenotypically distinct cell populations, and determines whether these populations stratify patient survival. With a pilot mass cytometry dataset of 2 million cells from 28 glioblastomas, RAPID identified tumor cells whose abundance independently and continuously stratified patient survival.

Training Novices in Generation and Analysis of High-Dimensional Human Cell Phospho-Flow Cytometry Data.

This article presents a single experiment designed to introduce a trainee to multiple advanced bench and analysis techniques, including high-dimensional cytometry, profiling cell signaling networks, functional assays with primary human tissue, and single-cell analysis with machine learning tools. The trainee is expected to have only minimal laboratory experience and is not required to have any prior training in flow cytometry, immunology, or data science. This article aims to introduce the advanced research areas with a design that is robust enough that novice trainees will succeed, flexible enough to allow some project customization, and fundamental enough that the skills and knowledge gained will provide a template for future experiments.