Honors & Awards
Science Graduate Student Research Awardee, Department of Energy (2018 - 2019)
NSF Graduate Research Fellow, National Science Foundation (2015 - 2018)
Doctor of Philosophy, California Institute of Technology, Chemistry (2022)
Bachelor of Science, University of California Berkeley, Chemistry (2012)
Current Research and Scholarly Interests
I currently explore the application of vibrational spectroscopic technologies for biomedical imaging and precision medicine for clinical use. My research interests are directly related to chemical imaging technology development, which include but are not limited to spectral and image processing and analysis, machine learning applications, autonomous adaptive data acquisition, and vibrational spectroscopic applications to the biomedical sciences.
- Toward implementing autonomous adaptive data acquisition for scanning hyperspectral imaging of biological systems APPLIED PHYSICS REVIEWS 2023; 10 (1)
Expanding hyperspectral imaging applications to the clinical scene: non-invasive, label-free approaches for early diagnostics and precision medicine
Frontiers in Imaging
View details for DOI 10.3389/fimag.2023.1175860
- MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies IEEE. 2022: 10-15
Autonomous adaptive data acquisition for scanning hyperspectral imaging
2020; 3 (1): 684
Non-invasive and label-free spectral microscopy (spectromicroscopy) techniques can provide quantitative biochemical information complementary to genomic sequencing, transcriptomic profiling, and proteomic analyses. However, spectromicroscopy techniques generate high-dimensional data; acquisition of a single spectral image can range from tens of minutes to hours, depending on the desired spatial resolution and the image size. This substantially limits the timescales of observable transient biological processes. To address this challenge and move spectromicroscopy towards efficient real-time spatiochemical imaging, we developed a grid-less autonomous adaptive sampling method. Our method substantially decreases image acquisition time while increasing sampling density in regions of steeper physico-chemical gradients. When implemented with scanning Fourier Transform infrared spectromicroscopy experiments, this grid-less adaptive sampling approach outperformed standard uniform grid sampling in a two-component chemical model system and in a complex biological sample, Caenorhabditis elegans. We quantitatively and qualitatively assess the efficiency of data acquisition using performance metrics and multivariate infrared spectral analysis, respectively.
View details for DOI 10.1038/s42003-020-01385-3
View details for Web of Science ID 000595691000001
View details for PubMedID 33208883
View details for PubMedCentralID PMC7676237
Sensitive Detection and Analysis of Neoantigen-Specific T Cell Populations from Tumors and Blood
2019; 28 (10): 2728-+
Neoantigen-specific T cells are increasingly viewed as important immunotherapy effectors, but physically isolating these rare cell populations is challenging. Here, we describe a sensitive method for the enumeration and isolation of neoantigen-specific CD8+ T cells from small samples of patient tumor or blood. The method relies on magnetic nanoparticles that present neoantigen-loaded major histocompatibility complex (MHC) tetramers at high avidity by barcoded DNA linkers. The magnetic particles provide a convenient handle to isolate the desired cell populations, and the barcoded DNA enables multiplexed analysis. The method exhibits superior recovery of antigen-specific T cell populations relative to literature approaches. We applied the method to profile neoantigen-specific T cell populations in the tumor and blood of patients with metastatic melanoma over the course of anti-PD1 checkpoint inhibitor therapy. We show that the method has value for monitoring clinical responses to cancer immunotherapy and might help guide the development of personalized mutational neoantigen-specific T cell therapies and cancer vaccines.
View details for DOI 10.1016/j.celrep.2019.07.106
View details for Web of Science ID 000484377200022
View details for PubMedID 31484081
High pCO(2)-induced exopolysaccharide-rich ballasted aggregates of planktonic cyanobacteria could explain Paleoproterozoic carbon burial
2018; 9: 2116
The contribution of planktonic cyanobacteria to burial of organic carbon in deep-sea sediments before the emergence of eukaryotic predators ~1.5 Ga has been considered negligible owing to the slow sinking speed of their small cells. However, global, highly positive excursion in carbon isotope values of inorganic carbonates ~2.22-2.06 Ga implies massive organic matter burial that had to be linked to oceanic cyanobacteria. Here to elucidate that link, we experiment with unicellular planktonic cyanobacteria acclimated to high partial CO2 pressure (pCO2) representative of the early Paleoproterozoic. We find that high pCO2 boosts generation of acidic extracellular polysaccharides (EPS) that adsorb Ca and Mg cations, support mineralization, and aggregate cells to form ballasted particles. The down flux of such self-assembled cyanobacterial aggregates would decouple the oxygenic photosynthesis from oxidative respiration at the ocean scale, drive export of organic matter from surface to deep ocean and sustain oxygenation of the planetary surface.
View details for DOI 10.1038/s41467-018-04588-9
View details for Web of Science ID 000433298800007
View details for PubMedID 29844378
View details for PubMedCentralID PMC5974010
FOAM (Functional Ontology Assignments for Metagenomes): a Hidden Markov Model (HMM) database with environmental focus
NUCLEIC ACIDS RESEARCH
2014; 42 (19): e145
A new functional gene database, FOAM (Functional Ontology Assignments for Metagenomes), was developed to screen environmental metagenomic sequence datasets. FOAM provides a new functional ontology dedicated to classify gene functions relevant to environmental microorganisms based on Hidden Markov Models (HMMs). Sets of aligned protein sequences (i.e. 'profiles') were tailored to a large group of target KEGG Orthologs (KOs) from which HMMs were trained. The alignments were checked and curated to make them specific to the targeted KO. Within this process, sequence profiles were enriched with the most abundant sequences available to maximize the yield of accurate classifier models. An associated functional ontology was built to describe the functional groups and hierarchy. FOAM allows the user to select the target search space before HMM-based comparison steps and to easily organize the results into different functional categories and subcategories. FOAM is publicly available at http://portal.nersc.gov/project/m1317/FOAM/.
View details for DOI 10.1093/nar/gku702
View details for Web of Science ID 000347689500001
View details for PubMedID 25260589
View details for PubMedCentralID PMC4231724
Synchrotron infrared imaging of advanced glycation endproducts (AGEs) in cardiac tissue from mice fed high glycemic diets
BIOMEDICAL SPECTROSCOPY AND IMAGING
2013; 2 (4): 301–15
Recent research findings correlate an increased risk for dieases such as diabetes, macular degeneration and cardiovascular disease (CVD) with diets that rapidly raise the blood sugar levels; these diets are known as high glycemic index (GI) diets which include white breads, sodas and sweet deserts. Lower glycemia diets are usually rich in fruits, non-starchy vegetables and whole grain products. The goal of our study was to compare and contrast the effects of a low vs. high glycemic diet using the biochemical composition and microstructure of the heart. The improved spatial resolution and signal-to-noise for SR-FTIR obtained through the coupling of the bright synchrotron infrared photon source to an infrared spectral microscope enabled the molecular-level observation of diet-related changes within unfixed fresh frozen histologic sections of mouse cardiac tissue. High and low glycemic index (GI) diets were started at the age of five-months and continued for one year, with the diets only differing in their starch distribution (high GI diet = 100% amylopectin versus low GI diet = 30% amylopectin/70% amylose). Serial cryosections of cardiac tissue for SR-FTIR imaging alternated with adjacent hematoxylin and eosin (H&E) stained sections allowed not only fine-scale chemical analyses of glycogen and glycolipid accumulation along a vein as well as protein glycation hotspots co-localizing with collagen cold spots but also the tracking of morphological differences occurring in tandem with these chemical changes. As a result of the bright synchrotron infrared photon source coupling, we were able to provide significant molecular evidence for a positive correlation between protein glycation and collagen degradation in our mouse model. Our results bring a new insight not only to the effects of long-term GI dietary practices of the public but also to the molecular and chemical foundation behind the cardiovascular disease pathogenesis commonly seen in diabetic patients.
View details for DOI 10.3233/BSI-130057
View details for Web of Science ID 000214729500005
View details for PubMedID 26500847
View details for PubMedCentralID PMC4617198