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42. Diversity of post-translational modifications and cell signaling revealed by single cell and single organelle mass spectrometry.

Author

Mun, Dong-Gi, Bhat, Firdous A., Joshi, Neha, Sandoval, Leticia, Ding, Husheng, Jain, Anu, Peterson, Jane A., Kang, Taewook, Pujari, Ganesh P., Tomlinson, Jennifer L., Budhraja, Rohit, Zenka, Roman M., Kannan, Nagarajan, Kipp, Benjamin R., Dasari, Surendra, Gaspar-Maia, Alexandre, Smoot, Rory L., Kandasamy, Richard K., and Pandey, Akhilesh

Subjects
POST-translational modification, SINGLE cell proteins, MASS spectrometry, CELL communication, PROTEIN-protein interactions, PROTEOMICS, ORGANELLES
Abstract

The rapid evolution of mass spectrometry-based single-cell proteomics now enables the cataloging of several thousand proteins from single cells. We investigated whether we could discover cellular heterogeneity beyond proteome, encompassing post-translational modifications (PTM), protein-protein interaction, and variants. By optimizing the mass spectrometry data interpretation strategy to enable the detection of PTMs and variants, we have generated a high-definition dataset of single-cell and nuclear proteomic-states. The data demonstrate the heterogeneity of cell-states and signaling dependencies at the single-cell level and reveal epigenetic drug-induced changes in single nuclei. This approach enables the exploration of previously uncharted single-cell and organellar proteomes revealing molecular characteristics that are inaccessible through RNA profiling. This study explored single cells and single organelles to reveal unique molecular characteristics such as mutant peptides and modulation of diverse post-translational modifications that are inaccessible through DNA/RNA profiling. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Monitoring Harmful Algal Blooms and Water Quality Using Sentinel-3 OLCI Satellite Imagery with Machine Learning.

Author

Joshi, Neha, Park, Jongmin, Zhao, Kaiguang, Londo, Alexis, and Khanal, Sami

Subjects
*ALGAL blooms, *PLANKTON blooms, *WATER quality, *REMOTE-sensing images, *DISSOLVED organic matter, *MICROCYSTINS, *MICROCYSTIS, *TOXIC algae
Abstract

Cyanobacterial harmful algal blooms release toxins and form thick blanket layers on the water surface causing widespread problems, including serious threats to human health, water ecosystem, economics, and recreation. To identify the potential drivers for the bloom, there is a need for extensive observations of the water sources with bloom occurrences. However, the traditional methods for monitoring water sources, such as collection of point ground samples, have proven limited due to spatial and temporal variability of water resources, and the cost associated with collecting samples that accurately represent this variability. These limitations can be addressed through the use of high-frequency satellite data. In this study, we explored the use of Random Forest (RF), which is one of the widely used machine learning architectures, to evaluate the performance of Sentinel-3 OLCI (Ocean and Land Color Imager) images in predicting bloom proxies in the western region of Lake Erie. The sixteen available bands of Sentinel-3 images were used as the predictor variables, while four proxies of the cyanobacterial masses, including Chlorophyll-a, Microcystin, Phycocyanin, and Secchi-depth, were considered as response variables in the RF models, with one RF model per proxy. Each of the proxies comes with a unique set of traits that can help with bloom detection. Among four RF models, the model for Chlorophyll-a performed the best with R2 = 0.55 and RMSE = 20.84 µg/L, while R2 performance for the rest of the other proxies was less than 0.5. This is because Chlorophyll-a is the most dominant and optically active pigment in water, while Phycocyanin, which is a strong indicator of harmful bloom, is present in low concentrations. Additionally, Microcystin, responsible for bloom toxicity, has limited spectral sensitivity, and Secchi-depth could be influenced by various factors besides blooms, such as colored dissolved organic and inorganic matter. On further examining the relationship between the proxies, Microcystin and Secchi-depth were significantly correlated with Chlorophyll-a, which enhances the usefulness of Chlorophyll-a in accurately identifying the presence of algal blooms. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Metabolomic biomarkers for (R, S)‐ketamine and (S)‐ketamine in treatment‐resistant depression and healthy controls: A systematic review.

Author

Kumar, Rakesh, Nuñez, Nicolas A., Joshi, Neha, Joseph, Boney, Verde, Alessandra, Seshadri, Ashok, Cuellar Barboza, Alfredo B., Prokop, Larry J., Medeiros, Gustavo C., and Singh, Balwinder

Subjects
METABOLOMICS, BIOMARKERS, MENTAL depression, BIPOLAR disorder, ENERGY metabolism
Abstract

Background: Ketamine is increasingly used for treatment‐resistant depression (TRD) while its mechanism of action is still being investigated. In this systematic review, we appraise the current evidence of metabolomic biomarkers for racemic ketamine and esketamine in patients with TRD and healthy controls (HCs). Methods: A comprehensive search of several databases (Ovid MEDLINE®, Embase, and Epub Ahead of Print) was performed from each database's inception to June 29, 2022, in any language, was conducted. We included studies wherein the metabolomic biomarkers for racemic ketamine or esketamine were investigated in TRD or HCs. Our main outcomes were to examine changes in metabolites among patients treated with ketamine/esketamine and explore the association with response to ketamine/esketamine. Results: A total of 1859 abstracts were screened of which 11 were included for full‐text review. Of these, a total of five articles were included (N = 147), including three RCTs (n = 129) and two open‐label trials (n = 18). All studies used racemic ketamine; one study additionally used esketamine. The included studies evaluated patients with treatment‐resistant bipolar depression (n = 22), unipolar depression (n = 91), and HCs (n = 34). The included studies reported alteration in several metabolites including acylcarnitines, lipids, kynurenine (KYN), and arginine with ketamine in TRD. Studies suggest the involvement of energy metabolism, KYN, and arginine pathways. In HCs, acetylcarnitine decreased post‐infusion, whereas inconsistent findings were observed after the ketamine infusion in TRD patients. Conclusions: This systematic review provides preliminary evidence that ketamine may cause changes in several important pathways involved in energy metabolism and inflammation. Larger and more rigorous studies are needed. [ABSTRACT FROM AUTHOR]

Published
2024
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