publications

2024

1. Science

   A panoramic view of cell population dynamics in mammalian aging

   Zehao Zhang, Chloe Schaefer, Weirong Jiang, and 11 more authors

   Science, 2024

   Abs DOI Bib

   To elucidate aging-associated cellular population dynamics, we present PanSci, a single-cell transcriptome atlas profiling over 20 million cells from 623 mouse tissues across different life stages, sexes, and genotypes. This comprehensive dataset reveals more than 3,000 unique cellular states and over 200 aging-associated cell populations. Our panoramic analysis uncovered organ-, lineage-, and sex-specific shifts of cellular dynamics during lifespan progression. Moreover, we identify both systematic and organ-specific alterations in immune cell populations associated with aging. We further explored the regulatory roles of the immune system on aging and pinpointed specific age-related cell population expansions that are lymphocyte dependent. Our “cell-omics” strategy enhances comprehension of cellular aging and lays the groundwork for exploring the complex cellular regulatory networks in aging and aging-associated diseases.

   @article{doi:10.1126/science.adn3949,
     author = {Zhang, Zehao and Schaefer, Chloe and Jiang, Weirong and Lu, Ziyu and Lee, Jasper and Sziraki, Andras and Abdulraouf, Abdulraouf and Wick, Brittney and Haeussler, Maximilian and Li, Zhuoyan and Molla, Gesmira and Satija, Rahul and Zhou, Wei and Cao, Junyue},
     title = {A panoramic view of cell population dynamics in mammalian aging},
     journal = {Science},
     volume = {0},
     number = {0},
     pages = {eadn3949},
     year = {2024},
     doi = {10.1126/science.adn3949},
     url = {https://www.science.org/doi/abs/10.1126/science.adn3949},
     eprint = {https://www.science.org/doi/pdf/10.1126/science.adn3949}
   }

2. Bioinformatics

   SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis

   Gohta Aihara, Kalen Clifton, Mayling Chen, and 6 more authors

   Bioinformatics, Jun 2024

   Abs DOI Bib

   Spatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells.To enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. We apply SEraster to both real and simulated spatial omics data prior to spatial variable gene expression analysis to demonstrate that such preprocessing can reduce computational resource requirements while maintaining high performance, including as compared to other down-sampling approaches. We further integrate SEraster with existing analysis tools to characterize cell-type spatial co-enrichment across length scales. Finally, we apply SEraster to enable analysis of a mouse pup spatial omics dataset with over a million cells to identify tissue-level and cell-type-specific spatially variable genes as well as spatially co-enriched cell types that recapitulate expected organ structures.SEraster is implemented as an R package on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials at https://JEF.works/SEraster.

   @article{10.1093/bioinformatics/btae412,
     author = {Aihara, Gohta and Clifton, Kalen and Chen, Mayling and Li, Zhuoyan and Atta, Lyla and Miller, Brendan F and Satija, Rahul and Hickey, John W and Fan, Jean},
     title = {SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis},
     journal = {Bioinformatics},
     volume = {40},
     number = {7},
     pages = {btae412},
     year = {2024},
     month = jun,
     issn = {1367-4811},
     doi = {10.1093/bioinformatics/btae412},
     url = {https://doi.org/10.1093/bioinformatics/btae412},
     eprint = {https://academic.oup.com/bioinformatics/article-pdf/40/7/btae412/58460631/btae412.pdf}
   }

2023

1. Nat Chem Biol

   Engineered bacterial swarm patterns as spatial records of environmental inputs

   Anjali Doshi, Marian Shaw, Ruxandra Tonea, and 6 more authors

   Aided in experiments and earned acknowledgements.

   Nature Chemical Biology, Jul 2023

   Abs DOI Bib

   A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility—a highly coordinated and rapid movement of bacteria powered by flagella. Engineering swarming is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. Here we engineer Proteus mirabilis, which natively forms centimeter-scale bullseye swarm patterns, to ‘write’ external inputs into visible spatial records. Specifically, we engineer tunable expression of swarming-related genes that modify pattern features, and we develop quantitative approaches to decoding. Next, we develop a dual-input system that modulates two swarming-related genes simultaneously, and we separately show that growing colonies can record dynamic environmental changes. We decode the resulting multicondition patterns with deep classification and segmentation models. Finally, we engineer a strain that records the presence of aqueous copper. This work creates an approach for building macroscale bacterial recorders, expanding the framework for engineering emergent microbial behaviors.

   @article{10.1093/bioinformatics/btae413,
     author = {Doshi, Anjali and Shaw, Marian and Tonea, Ruxandra and Moon, Soonhee and Minyety, Rosalía and Doshi, Anish and Laine, Andrew and Guo, Jia and Danino, Tal},
     title = {Engineered bacterial swarm patterns as spatial records of environmental inputs},
     journal = {Nature Chemical Biology},
     volume = {19},
     number = {7},
     pages = {878-886},
     year = {2023},
     month = jul,
     doi = {10.1038/s41589-023-01325-2},
     url = {https://doi.org/10.1038/s41589-023-01325-2},
     eprint = {https://www.nature.com/articles/s41589-023-01325-2},
   }