Bryan J. Cannon is a graduate student at Stanford University, pursuing a PhD in Computational & Systems Immunology, with research focused on studying the cellular and acellular composition of human neurodegeneration using hi-dimensional imaging and sequencing datasets. He has experience in computational immunology, including multiplex ion beam imaging technology, image segmentation, and multi-dimensional analysis pipelines, as well as expertise in R, Matlab, and Python programming languages. Prior to Stanford, he worked as a Project Associate at NASA Ames Research Center and a Research Assistant at the Autoimmune & Rheumatology Lab, Bone Research Lab, and Cardiac Surgery Lab. Additionally, he has been involved in advocacy work, including mentoring high school students in summer research, working on a project for diversity and inclusion in immunology, giving lectures for the EXPLORE Lecture Series, and mentoring first-generation and low-income students at Stanford.
Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering.
2023; 14 (1): 4618
While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge. To understand the spatial organization of tissue and how it relates to disease processes, imaging studies typically focus on cell-level phenotypes. However, images can capture biologically important objects that are outside of cells, such as the extracellular matrix. Here, we describe a pipeline, Pixie, that achieves robust and quantitative annotation of pixel-level features using unsupervised clustering and show its application across a variety of biological contexts and multiplexed imaging platforms. Furthermore, current cell phenotyping strategies that rely on unsupervised clustering can be labor intensive and require large amounts of manual cluster adjustments. We demonstrate how pixel clusters that lie within cells can be used to improve cell annotations. We comprehensively evaluate pre-processing steps and parameter choices to optimize clustering performance and quantify the reproducibility of our method. Importantly, Pixie is open source and easily customizable through a user-friendly interface.
View details for DOI 10.1038/s41467-023-40068-5
View details for PubMedID 37528072
View details for PubMedCentralID 6086938
Expanded vacuum-stable gels for multiplexed high-resolution spatial histopathology.
2023; 14 (1): 4013
Cellular organization and functions encompass multiple scales in vivo. Emerging high-plex imaging technologies are limited in resolving subcellular biomolecular features. Expansion Microscopy (ExM) and related techniques physically expand samples for enhanced spatial resolution, but are challenging to be combined with high-plex imaging technologies to enable integrative multiscaled tissue biology insights. Here, we introduce Expand and comPRESS hydrOgels (ExPRESSO), an ExM framework that allows high-plex protein staining, physical expansion, and removal of water, while retaining the lateral tissue expansion. We demonstrate ExPRESSO imaging of archival clinical tissue samples on Multiplexed Ion Beam Imaging and Imaging Mass Cytometry platforms, with detection capabilities of>40 markers. Application of ExPRESSO on archival human lymphoid and brain tissues resolved tissue architecture at the subcellular level, particularly that of the blood-brain barrier. ExPRESSO hence provides a platform for extending the analysis compatibility of hydrogel-expanded biospecimensto mass spectrometry, with minimal modifications to protocols and instrumentation.
View details for DOI 10.1038/s41467-023-39616-w
View details for PubMedID 37419873
Spatial proteomics reveals human microglial states shaped by anatomy and neuropathology.
Microglia are implicated in aging, neurodegeneration, and Alzheimer's disease (AD). Traditional, low-plex, imaging methods fall short of capturing in situ cellular states and interactions in the human brain. We utilized Multiplexed Ion Beam Imaging (MIBI) and data-driven analysis to spatially map proteomic cellular states and niches in healthy human brain, identifying a spectrum of microglial profiles, called the microglial state continuum (MSC). The MSC ranged from senescent-like to active proteomic states that were skewed across large brain regions and compartmentalized locally according to their immediate microenvironment. While more active microglial states were proximal to amyloid plaques, globally, microglia significantly shifted towards a, presumably, dysfunctional low MSC in the AD hippocampus, as confirmed in an independent cohort (n=26). This provides an in situ single cell framework for mapping human microglial states along a continuous, shifting existence that is differentially enriched between healthy brain regions and disease, reinforcing differential microglial functions overall.
View details for DOI 10.21203/rs.3.rs-2987263/v1
View details for PubMedID 37398389
View details for PubMedCentralID PMC10312937
Single-cell spatial proteomic imaging for human neuropathology.
Acta neuropathologica communications
2022; 10 (1): 158
Neurodegenerative disorders are characterized by phenotypic changes and hallmark proteopathies. Quantifying these in archival human brain tissues remains indispensable for validating animal models and understanding disease mechanisms. We present a framework for nanometer-scale, spatial proteomics with multiplex ion beam imaging (MIBI) for capturing neuropathological features. MIBI facilitated simultaneous, quantitative imaging of 36 proteins on archival human hippocampus from individuals spanning cognitively normal to dementia. Customized analysis strategies identified cell types and proteopathies in the hippocampus across stages of Alzheimer's disease (AD) neuropathologic change. We show microglia-pathologic tau interactions in hippocampal CA1 subfield in AD dementia. Data driven, sample independent creation of spatial proteomic regions identified persistent neurons in pathologic tau neighborhoods expressing mitochondrial protein MFN2, regardless of cognitive status, suggesting a survival advantage. Our study revealed unique insights from multiplexed imaging and data-driven approaches for neuropathologic analysis and serves broadly as a methodology for spatial proteomic analysis of archival human neuropathology. TEASER: Multiplex Ion beam Imaging enables deep spatial phenotyping of human neuropathology-associated cellular and disease features.
View details for DOI 10.1186/s40478-022-01465-x
View details for PubMedID 36333818
B cells in rheumatoid arthritis synovial tissues encode focused antibody repertoires that include antibodies that stimulate macrophage TNF-α production.
Clinical immunology (Orlando, Fla.)
Rheumatoid arthritis (RA) is characterized by the production of anti-citrullinated protein antibodies (ACPAs). To gain insights into the relationship between ACPA-expressing B cells in peripheral blood (PB) and synovial tissue (ST), we sequenced the B cell repertoire in paired PB and ST samples from five individuals with established, ACPA+ RA. Bioinformatics analysis of paired heavy and light chain sequences revealed clonally-related family members shared between PB and ST. ST-derived antibody repertoires exhibited reduced diversity and increased normalized clonal family size compared to PB-derived repertoires. Functional characterization showed that seven recombinant antibodies (rAbs) expressed from subject-derived sequences from both compartments bound citrullinated antigens and immune complexes (ICs) formed using one ST-derived rAb stimulated macrophage TNF-α production. Our findings demonstrate B cell trafficking between PB and ST in subjects with RA and ST repertoires include B cells that encode ACPA capable of forming ICs that stimulate cellular responses implicated in RA pathogenesis.
View details for DOI 10.1016/j.clim.2020.108360
View details for PubMedID 32035179
- Affinity Maturation Drives Epitope Spreading and Generation of Proinflammatory Anti-Citrullinated Protein Antibodies in Rheumatoid Arthritis ARTHRITIS & RHEUMATOLOGY 2018; 70 (12): 1946–58