Soft X-ray Tomography (SXT) is an emerging technique for rapidly mapping ultrastructure in whole cells. Given the potential of SXT to contribute to whole-cell modeling and study the effects of drugs on cellular ultrastructure, it is important to develop methods for rapid quantification and characterization of organelles. Our lab works on developing a toolkit to extract helpful information from SXT data.

Collaborators: Prof. Kate White (USC) and Prof. Carolyn Larabell (UCSF)

Publications:

• White, K. L., Singla, J., Loconte, V., Chen, J. H., Ekman, A., Sun, L., Zhang, X., Francis, J. P., Li, A., Lin, W., Tseng, K., McDermott, G., Alber, F., Sali, A., Larabell, C., & Stevens, R. C. (2020). Visualizing subcellular rearrangements in intact β cells using soft x-ray tomography. Science advances, 6(50), eabc8262.

  • • • Highlighted in Science: Subcellular map of vesicle maturation

• Loconte, V., Singla, J., Li, A., Chen, J.H., Ekman, A., McDermott, G., Sali, A., White, K.L. & Larabell, C.A. (2022). Soft X-ray tomography to map and quantify organelle interactions at the mesoscale. Structure, 30(4), 510-521.E3.

  • • • Highlighted in Nature: Soft X-rays capture the dance of the organelles

• Li, A., Zhang, X., Singla, J., White, K.L., Loconte, V., Hu, C., Zhang, C., Li, S., Li, W., Francis, J.P., Sali, A., Wang, C., Sun, L., He, X. & Stevens, R.C. (2022). Auto-segmentation and time-dependent systematic analysis of mesoscale cellular structure in β-cells during insulin secretion. PLOS One, 17(3), e0265567.

• Li, A., Zhang, S., Loconte, V., Liu, Y., Ekman, A., Thompson, G.J., Sali, A., Stevens, R.C., White, K., Singla, J., Sun, L. (2022). An intensity-based post-processing tool for 3D instance segmentation of organelles in Soft X-ray tomograms. PLOS One, 17(9), e0269887.

Cryo-Electron Tomography (Cryo-ET) enables 3D visualization of cells in a near-native state at molecular resolution. The produced cellular tomograms contain detailed information about many macromolecular complexes, their structures, abundances, and specific spatial locations in the cell. However, systematically extracting this information is very challenging, and current methods usually rely on individual templates of known structures. Our lab works on developing a framework for template-free unsupervised discovery of different complexes from highly heterogeneous sets of particles extracted from entire cellular tomograms.

Collaborators: Prof. Frank Alber (UCLA)

Publications:

• Xu, M., Singla, J., Tocheva, E. I., Chang, Y. W., Stevens, R. C., Jensen, G. J., & Alber, F. (2019). De novo structural pattern mining in cellular electron cryotomograms. Structure, 27(4), 679-691.

  • • • Highlighted in Nature Methods: Template-free visual proteomics

• Singla, J., White, K. L., Stevens, R. C., & Alber, F. (2021). Assessment of scoring functions to rank the quality of 3D subtomogram clusters from cryo-electron tomography. Journal of Structural Biology, 213(2), 107727.

As part of a project to build a spatiotemporal model of the pancreatic b-cell, we are creating an immersive experience called ‘‘World in a Cell’’ that can be used to integrate and create new educational tools. To do this, we are developing a new visual design language that uses tetrahedral building blocks to express the structural features of biological molecules and organelles in crowded cellular environments. The tetrahedral language enables more efficient animation and user interaction in an immersive environment.

Collaborators: Prof. Helen Berman (Rutgers University) and Prof. Alex McDowell (USC)

Publications:

• Singla, J., Burdsall, K., Cantrell, B., Halsey, J. R., McDowell, A., McGregor, C., ... & Berman, H. M. (2022). A New Visual Design Language for Biological Structures in a Cell. Structure, 30(4), 485-497.E3.

Jaundice occurs in 60-80% of neonates worldwide and is one of the most common morbidities in both term and preterm neonates. Detecting Jaundice in neonates using non-invasive methods have been under development for some time across the world. We are generating a large corpus of Indian dataset and ML-based methods to detect neonatal jaundice.

Collaborators:

PGI Chandigarh: Dr. Venkataseshan Sundaram (Newborn Unit)

IIT Roorkee: Sugata Gangopadhyay, Prof. Aditi Gangopadhyay, Prof. Sandeep Garg