Remote collaboration today rarely involves a single communication channel. Instead, teams frequently juggle a myriad of communication tools, such as video conferencing, group chat, and email. Each of these platforms provides different mechanisms for relaying information and media to ultimately meet the needs and goals of the team. While discussions occurring on different platforms are often related, existing tools used to support each type of communication are disconnected. To research how to bridge this gap and support seamless collaboration and communication across different platforms, we developed a toolkit that connects conversations between three of the most commonly used remote collaboration platforms: Slack, Google Docs, and Zoom, covering both synchronous and asynchronous modes of communication. We iteratively designed and implemented features such as adding information from Slack chat directly to Google Docs notes to build up meeting agendas and selecting specific snippets of Zoom meetings to be embedded into notes or sent to chat. We also plan to evaluate the effectiveness of our toolkit in helping streamline the transfer of information across different team communication sites and enhancing the remote collaboration experience for teams via subsequent qualitative user studies. Specifically, we will be conducting a week-long field study with existing teams, such as teams from industry, teams working on school projects, research groups, committees, etc. We will use a combination of experience sampling, diary study and post-study interviews to understand their experience. The results we expect to get from these exploratory user studies will help us answer the following questions: Which aspects of the tools work best for the users? Does the current UI and design make sense for how the user interacts with the toolkit? In which scenarios is the toolkit being used most effectively? These results will also guide us in designing additional features for the toolkit in the future.
 

Merkel cell carcinoma (MCC) is a rare and aggressive skin cancer with a mortality rate of ~30%. In ~80% of cases, MCC development is attributed to the integration of Merkel cell polyomavirus (MCPyV) DNA into the host’s genome, leading to the expression of viral oncoproteins and tumorigenesis. Developing treatments that sustain immunity against MCC is imperative to address recurrent and/or progressive disease. In many cancers, tumor-infiltrating B cells have been associated with better prognosis and response to immunotherapies. However, the mechanisms by which B cells contribute to tumor immunity in humans have been difficult to resolve in part due to the inter-patient heterogeneity of tumor-specific antigens. The shared nature of MCPyV tumor antigens in MCC allows for MCC-specific B cell responses to be studied across patients. Using DNA-barcoded and fluorescently labeled viral oncoprotein tetramers, we analyzed the transcriptome, proteome, and receptor repertoire of MCC tumor-infiltrating B cells in 12 patient samples at single-cell resolution. From paired heavy and light chain sequences, we cloned 8 antibodies from B cells specific for the MCPyV oncoproteins to confirm binding to MCC-specific antigens. Transcriptomic and proteomic analyses of MCPyV-specific B cells revealed heterogeneity of intra-tumoral B cell responses. Interestingly, we found that the absence of MCC-specific germinal center (GC) B cells in MCC tumors associates with disease progression: ~80% of patients with no detectable GC B cells had MCC progression within a year post-surgery, whereas patients with detectable GC B cells remained progression-free a year after surgery (n=12, p=0.0043). These results suggest strong synergy between B cells and T cells may regulate tumor growth, as B cells rely on signals presented by T cells to differentiate into GC cells. Our long-term objective is to identify B cell phenotypes associated with anti-MCC responses to develop therapeutics that boost cancer-specific immunity.

Glutamine synthetase (GS) is a highly regulated enzyme that catalytically converts glutamate to glutamine which is associated with ammonia assimilation. One of the effects of dysregulation in the GS inter-conversion process is hyperammonemia, which can cause death or brain damage. GS is conserved across all prokaryotes and eukaryotes. Among enzymes, glutamine synthetase's ability to polymerize is still a structural mystery and the functional characteristics of its self-assembling filaments remain unknown. The aim is to understand the occurrence of filament formation in GS and the effects on enzyme activity. We hypothesized that filaments may influence the association of GS substrates or allosterically regulate GS. I purified the GS of Pseudomonas aeruginosa, Mycobacterium tuberculosis, and Helicobacter pylori by using Ni-column and size exclusion chromatography (SEC). Then, I examined the GS of pseudomonas under different buffer conditions (Mg2+, Co2+) using negative staining. Under Magnesium (10mM) conditions, the known dodecamer structure of GS was observed. Under Cobalt (10mM) conditions, the filament was being induced. To better investigate the structural mechanism of filament formation we turned to cryogenic electron microscopy (Cryo-EM). The next step is to create a model of the filament interface of GS and identify the residues involved. This research has broad implications in the field of metabolic engineering, as understanding the structure and the role of filament formation in GS could help develop new therapeutic targets in metabolism.

Intermediate metabolism in cells has generally been studied without considering the arrangement of enzymes within the cell. However, recent developments have shown that many metabolic enzymes form organization systems that are made up of oligomers stacking linearly into filaments. The enzyme phosphoribosyl pyrophosphate synthetase (PRPS) makes a precursor required for all de novo nucleotide synthesis in cells, and therefore plays an important role in cellular metabolism. This project aims to characterize the PRPS protein in Xenopus tropicalis and Giardia lamblia. We hypothesize that PRPS from X. tropicalis will have similar biochemical and structural properties as compared to human PRPS, while PRPS from G. lamblia will have different biochemical and structural properties. This hypothesis is supported by the small evolutionary difference between PRPS from humans and X. tropicalis as compared to the large evolutionary difference between PRPS from humans and G. lamblia. This difference would be especially interesting to examine from the perspective of filament formations in the PRPS protein. So far, we have created the Xenopus tropicalis and Giardia lamblia plasmids by cloning. Test expressions of the X. tropicalis yielded protein expression in E. coli cell strains C43 and RIL, while test expressions for G. lamblia have been successful in C43, BL21, pLysS, and Rosetta cell strains. This demonstrates that both X. tropicalis and G. lamblia PRPS can be expressed in E. Coli strains. An analysis of X. tropicalis will allow us to test how filament formation changes with only small evolutionary differences in PRPS. It could also be used for further research in vivo using frog eggs that act as a singular cell system. If it is confirmed that the G. lamblia protein is different from human PRPS, PRPS in G. lamblia could serve as an antibiotic target since current methods of treatment for the organism are very harmful to the human microbiome.

Genetically encoded fluorescent indicators (GEFI) change fluorescence level under microscope following a conformational change when bound to a target molecule, and can be used to visualize spatiotemporally specific biological processes involving a targeted molecule. Various imaging analysis tools exist to analyze the non-temporal fluorescent cell data, however there was no industry standard for the pipeline used to analyze molecular dynamics when imaged with GEFI in time-series experiments. This project aimed to develop a computational pipeline that analyzed the fluorescent readout of single cells in spatiotemporal experiments that utilized GEFI. The pipeline included both segmentation of cells, utilizing Cellpose, an existing deep learning-based generalizable and highly efficient segmentation program, and tracking of single cells across all frames. I personally contributed to the design, implementation, and testing of the tracking component of the pipeline. The tracking algorithm was designed using unsupervised machine learning, specifically k-means clustering with convolutional neural network feature extraction techniques. The pipeline was implemented using Python and made available and open source, accessible through Google Colaboratory for a more user friendly version, as well as Github for more thorough documentation and generalizability. Ultimately, this project aimed to minimize bias to result in more accurate and efficient high-throughput investigation of molecular dynamics when using fluorescent probes for dynamic cell imaging. Preliminary results demonstrated the effectiveness of the pipeline in tracking cells across various time points and provided a foundation for future optimizations and applications.