Technology

Technologies used in the lab

Lumine xMAP cytokine multiplex assays

What we do

We use xMAP technology by Luminex to detect cytokine expression and secretion in lymphocyte samples from mouse and human sources. With the available technology we can detect over 60 different cytokines in a sample as small as 10 ul. We are interested in cytokine expression in human diseases, before and after immunization and in the healthy general population. We also study various mouse models of autoimmune diseases.
This multiplexed cytokine assay allows generation of large amounts of data and therefore new analyses which result in new insights into the roles of cytokines in health and disease.

How this works

Luminex color-codes tiny beads, called microspheres, into (potentially) 100 distinct sets. Each bead set is then coated with an antibody to a specific cytokine or chemokine allowing the capture and detection of specific analytes from a sample. Within the Luminex compact analyzer, lasers excite the internal dyes that identify each microsphere particle, and also any reporter dye captured during the assay. Many readings are made on each bead set, further validating the results. In this way, xMAP technology allows multiplexing of up to 100 unique assays within a single small volume sample, both rapidly and precisely.

Tonsil organoid system

Tonsil organoid system

The screening of new vaccines and drugs is a slow and expensive process. Almost all drug testing needs to be performed first in animal models before moving onto human clinical trials. In addition to the limitations inherent of this long process, animal models can be poorly predictive of human immune response and toxicity. To bridge this gap, we developed an in vitro model of the human adaptive immune response: the tonsil organoid system (Wagar LE, et. al., Nat Med (2021)).

For this purpose we use human tonsils, a readily available lymphoid organ to develop a fully-functional human organoid. We have shown that the tonsil organoid system replicates key mechanisms of the germinal center in vitro upon stimulation with different vaccines, including antigen-specific antibody production, somatic hypermutation, affinity maturation, plasmablast differentiation and class switch recombination. In addition, this in vitro system can be selectively manipulated, providing an excellent opportunity for mechanistic studies to characterize immune responses to different vaccines or pathogens in a fully human system. Finally, the system has the potential to be scaled up for high throughput screening. We are using the tonsil organoid system for screening novel adjuvants and the development of vaccine candidates against several pathogens including SARS-CoV-2. We believe that screening using this in vitro human organoid system provides an ideal platform to rapidly prioritize lead candidate molecules for translation into human clinical studies.

Figure: (Left panel) Representative stereoscope images of day 7 unstimulated or live-attenuated influenza vaccine (LAIV) stimulated tonsil organoids. (Right panel) Confocal microscope images of day 7 unstimulated or LAIV stimulated tonsil organoids.

Codex

CODEX (CO-Detection by indEXin)

The spatial inter-relationship between cells plays a key role in determining function. Traditional immunohistochemistry (IHC) provides this spatial information but is limited to a few biomarkers, thus in turn few cell types per sample. The highly multiplexed proteomic imaging technology CODEX® System by Akoya Biosciences (CO-Detection by indEXin) enables imaging of 40+ protein biomarkers to define cellular subsets while preserving spatial resolution. Our lab is leveraging this technology to gain insights into the biology of secondary lymphoid organs and cancer biopsies. It allows us to study different cell phenotypes, in situ interactions and their impact on immune mechanisms that govern disease pathology and progression. We have successfully adapted this technology to analyze both FFPE and frozen tissue sections, and also human organoids. CODEX uses antibodies conjugated to a proprietary library of oligonucleotides called “barcodes” that we have customized to identify rare cell types. More importantly, we have developed image processing tools using deep learning algorithms that de-noise, remove autofluorescence, segment nuclei with high accuracies and define cell boundaries using nuclei and membrane biomarker signals. We have also built spatial analysis tools that cluster by cell phenotypes, calculate cell-cell distances, determine neighbor cell components, calculate cytokine gradients over distance or neighbor component, and identifies niches and physiological states.

Figure: CODEX imaging of a human tonsil tissue with germinal centers showing B and T cell interaction.

Mass Cytometry

Mass cytometry is a novel, highly multiparametric flow cytometry platform that uses antibodies conjugated to heavy metal ion tags to label cellular proteins and mass spectrometry for detection, instead of fluorescent tags and optical readouts. This affords the advantage of being able to interrogate many more parameters of cellular phenotype and function with little or no spillover between labels. Such capacity makes mass cytometry ideal for studying the complexity of the immune system and for describing it in detail by collecting information about individual cells within the natural milieu of blood or tissue. Using computational methodologies for visualization and analysis of these high dimensional data, we are then able to make inferences about the various cellular subsets and their relationships to other cells based on their complement of protein expression. Cell diversity and rare populations are also more readily visualized using these approaches than with other more population-based methodologies traditionally employed.

Systems Immunology

This approach allows us to study the human immune system through direct analysis of blood and tissue samples. Through the comprehensive study of healthy individuals, and in turn disease cohorts, we are beginning to map the immune system as a whole organ system to better understand how the different components work together and communicate to mount a response to a challenge, such as an infection or vaccination. Using systems immunology methodologies, we are studying the effects of other factors such as genetics, environment and aging on the function and potency of immune components. We are striving to develop profiles of protein expression for cellular subsets and identify biomarkers of immune health and disease that can be used to compare to and track within other populations in the developing world.

Single T cell analysis

As part of our efforts towards characterizing the various cellular components of the human immune system, we have developed and applied a variety of single cell assays. Besides mass cytometry and peptide-MHC tetramers (both described elsewhere) we are now able to perform single T cell RNA-sequencing (RNA-seq) to determine the specific alpha and beta chains of T cell receptors (TCR) expressed on individual cells. The TCR is the fundamental mechanism by which our adaptive immune system responds to an infectious agent such as bacteria or virus, and the alpha and beta chains work together to recognize fragments of antigen that are required for T cell activation. By deciphering the TCR sequence code, we will be able to design better vaccines and treatments for autoimmunity. Furthermore, through the use of nested PCR and barcoding techniques, we are also able to determine the functional capability of each T cell by looking at transcription factor and cytokine expression simultaneously. This is a powerful technique that provides detailed information about each T cell sequenced.

GLIPH

GLIPH (grouping lymphocyte interactions by paratope hotspots)

GLIPH is an algorithm that we have developed to analyze TCR sequences at an unprecedented scale. GLIPH2 can now analyze up to 10 million TCR sequences over a few days and using shared motifs and conserved CDR3 region sequence comparisons, group TCR sequences that have a high (80-90%) probability of recognizing the same or similar peptide-MHC and also predicting the correct restricting HLA allele. Global convergence is calculated by using an unselected naïve TCR sequence reference dataset and scoring using the hamming distance between two sequences. The lowest global similarity is between the two sets is recorded. Local convergence is calculated by collecting the 2mer, 3mer, 4mer or 5mer that is obtained in the input dataset. After global and local motifs clusters are generated, they are scored based on the combined categories of V-gene enrichment, CDR3 length distribution, clonal expansion, and HLA allele when available, with scores generated by a Simpson Diversity Index (Glanville J, Huang H, et. al., Nature (2017)).

We have successfully implemented GLIPH2 to analyze TCRb sequences from individuals (N=58) latently infected with Mycobacterium tuberculosis and to group them according to their specificity (Huang H, Wang C, et. al., Nat Biotech (2020)). More recently, we employed GLIPH2 to reliably parse TCRb sequences from 178 non-small cell lung cancer patients to identify 435 shared specificity groups that were clonally expanded and enriched in tumors compared to adjacent lung (Chiou SH, Tseng D, et. al., Immunity (2021)), demonstrating the wide applicability of our algorithm.

Figure: TCR specificity groups and predicted HLA-restriction among M. tuberculosis infected subjects. CDR3 α/β amino acid sequences from five GLIPH TCR specificity groups. Yellow-colored boxes highlight the predicted common HLA class II alleles for each specificity group (combinatorial sampling probability <0.013 DRB1*15 for group II, probability <0.007 DRB1*03 for group III, probability <0.03 DRB3*03 for group IV, probability <0.02 DRB1*15/DRB5*01 for group V). Green-colored boxes highlight the TCRs that have been validated in vitro. Red outlines indicate actual HLA as determined by reporter assay. Adapted from Glanville et. al., Nature 2017.

Peptide-MHC tetramers

T cell activation is dependent on recognition of its cognate antigen and occurs when bits of the antigen, called peptides, are presented to the T cell in the context of a receptor on the surface of antigen presenting cells. The peptide-presenting receptor, known as an MHC molecule, can be derived in vitro and loaded with peptides. These peptide-MHC complexes can then be joined into tetramers and used to survey blood samples that may contain T cells bearing receptors that will specifically recognize the tetramer. Once bound to the tetramer, these T cells can then be characterized according to their phenotype and functional capacity in order to elucidate their role in a particular immune response and their potential as targets for drugs or for immunotherapy.

Peptide-MHC spheromers

Peptide-MHC Spheromers

We have developed a T cell staining reagent that is superior to the tetramer technology that we introduced 25 years ago. We engineered a ferritin backbone from the hyperthermophilic archaeal anaerobe Pyrococcus furiosus to display 12 peptide-MHC molecules. These “spheromers” bind both MHC-I and MHC-II restricted T cells with excellent specificity for pMHC, and at a significantly higher avidity than the tetramer. They are able to label specific T cells more efficiently and capture a broader portion of the sequence repertoire for a given peptide-MHC than any other available platform. We have successfully used the spheromer technology to study SARS-CoV-2 specific T cell responses. We find that T cells recognizing peptides conserved amongst coronaviruses are more abundant in unexposed individuals and tend to have a “memory” phenotype, compared to those unique to SARS-CoV-2. Significantly, CD8+ T cells with these conserved specificities are much more abundant in COVID-19 patients with mild disease versus those with a more severe illness, suggesting a protective role. This “spheromer” platform offers several advantages: ease of production, defined site-specific conjugation of pMHC molecules that significantly reduces inter-batch variation, and compatibility with currently available pMHC molecules and streptavidin reagents allowing for facile translation.

Figure: Assembly of the “spheromer”. (A) Molecular surface representation of pMHC (PDB ID: 3TO2, a-chain in light blue, b2m in dark blue and peptide in yellow) and SAv (PDB ID: 2RTG, monomer in red and rest in coral). (B) Model of a semi-saturated SAv-pMHC₂ intermediate that has two unoccupied biotin binding sites. A single orientation is shown for simplicity. (C) A model of spheromer that is assembled by the conjugation of six semi-saturated SAv-pMHC₂ molecules onto a functionalized maxi-ferritin scaffold (PDB ID:2JD6, grey). UCSF Chimera was used for molecular graphics.

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