1
|
Wang Y, Thistlethwaite W, Tadych A, Ruf-Zamojski F, Bernard DJ, Cappuccio A, Zaslavsky E, Chen X, Sealfon SC, Troyanskaya OG. Automated single-cell omics end-to-end framework with data-driven batch inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.01.564815. [PMID: 37961197 PMCID: PMC10635042 DOI: 10.1101/2023.11.01.564815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
To facilitate single-cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI's data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/.
Collapse
Affiliation(s)
- Yuan Wang
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
- These authors contributed equally
| | - William Thistlethwaite
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
- These authors contributed equally
| | - Alicja Tadych
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
| | | | - Daniel J Bernard
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, H3G 1Y6, Canada
| | - Antonio Cappuccio
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xi Chen
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
| | - Stuart C. Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Olga G. Troyanskaya
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
- Lead contact
| |
Collapse
|
2
|
Rubenstein AB, Smith GR, Zhang Z, Chen X, Chambers TL, Ruf-Zamojski F, Mendelev N, Cheng WS, Zamojski M, Amper MAS, Nair VD, Marderstein AR, Montgomery SB, Troyanskaya OG, Zaslavsky E, Trappe T, Trappe S, Sealfon SC. Integrated single-cell multiome analysis reveals muscle fiber-type gene regulatory circuitry modulated by endurance exercise. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.26.558914. [PMID: 37808658 PMCID: PMC10557702 DOI: 10.1101/2023.09.26.558914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Endurance exercise is an important health modifier. We studied cell-type specific adaptations of human skeletal muscle to acute endurance exercise using single-nucleus (sn) multiome sequencing in human vastus lateralis samples collected before and 3.5 hours after 40 min exercise at 70% VO2max in four subjects, as well as in matched time of day samples from two supine resting circadian controls. High quality same-cell RNA-seq and ATAC-seq data were obtained from 37,154 nuclei comprising 14 cell types. Among muscle fiber types, both shared and fiber-type specific regulatory programs were identified. Single-cell circuit analysis identified distinct adaptations in fast, slow and intermediate fibers as well as LUM-expressing FAP cells, involving a total of 328 transcription factors (TFs) acting at altered accessibility sites regulating 2,025 genes. These data and circuit mapping provide single-cell insight into the processes underlying tissue and metabolic remodeling responses to exercise.
Collapse
Affiliation(s)
- Aliza B. Rubenstein
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
| | - Gregory R. Smith
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
| | - Zidong Zhang
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Xi Chen
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA
| | - Toby L. Chambers
- Human Performance Laboratory, Ball State University, Muncie, IN 47306, USA
| | - Frederique Ruf-Zamojski
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Natalia Mendelev
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
| | - Wan Sze Cheng
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
| | - Michel Zamojski
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Mary Anne S. Amper
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
| | - Venugopalan D. Nair
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
| | - Andrew R. Marderstein
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Stephen B. Montgomery
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Olga G. Troyanskaya
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
| | - Todd Trappe
- Human Performance Laboratory, Ball State University, Muncie, IN 47306, USA
| | - Scott Trappe
- Human Performance Laboratory, Ball State University, Muncie, IN 47306, USA
- Senior author
| | - Stuart C. Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY 10029, USA
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
| |
Collapse
|