1
|
Wang TC, Sawhney S, Morgan D, Bennett RL, Rashmi R, Estecio MR, Brock A, Singh I, Baer CF, Licht JD, Lele TP. Genetic variation drives cancer cell adaptation to ECM stiffness. Proc Natl Acad Sci U S A 2024; 121:e2403062121. [PMID: 39302966 PMCID: PMC11441511 DOI: 10.1073/pnas.2403062121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 08/26/2024] [Indexed: 09/22/2024] Open
Abstract
The progression of many solid tumors is accompanied by temporal and spatial changes in the stiffness of the extracellular matrix (ECM). Cancer cells adapt to soft and stiff ECM through mechanisms that are not fully understood. It is well known that there is significant genetic heterogeneity from cell to cell in tumors, but how ECM stiffness as a parameter might interact with that genetic variation is not known. Here, we employed experimental evolution to study the response of genetically variable and clonal populations of tumor cells to variable ECM stiffness. Proliferation rates of genetically variable populations cultured on soft ECM increased over a period of several weeks, whereas clonal populations did not evolve. Tracking of DNA barcoded cell lineages revealed that soft ECM consistently selected for the same few variants. These data provide evidence that ECM stiffness exerts natural selection on genetically variable tumor populations. Soft-selected cells were highly migratory, with enriched oncogenic signatures and unusual behaviors such as spreading and traction force generation on ECMs with stiffness as low as 1 kPa. Rho-regulated cell spreading was found to be the directly selected trait, with yes-associated protein 1 translocation to the nucleus mediating fitness on soft ECM. Overall, these data show that genetic variation can drive cancer cell adaptation to ECM stiffness.
Collapse
Affiliation(s)
- Ting-Ching Wang
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX77843
| | - Suchitaa Sawhney
- Department of Biomedical Engineering, Texas A&M University, College Station, TX77843
| | - Daylin Morgan
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX78712
| | - Richard L. Bennett
- Division of Hematology and Oncology, University of Florida Health Cancer Center, Gainesville, FL32610
| | - Richa Rashmi
- Department of Cell Biology and Genetics, Texas A&M University, Bryan, TX77807
| | - Marcos R. Estecio
- Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX77030
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX78712
| | - Irtisha Singh
- Department of Biomedical Engineering, Texas A&M University, College Station, TX77843
- Department of Cell Biology and Genetics, Texas A&M University, Bryan, TX77807
| | - Charles F. Baer
- Department of Biology, University of Florida, Gainesville, FL32611
| | - Jonathan D. Licht
- Division of Hematology and Oncology, University of Florida Health Cancer Center, Gainesville, FL32610
| | - Tanmay P. Lele
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX77843
- Department of Biomedical Engineering, Texas A&M University, College Station, TX77843
- Department of Translational Medical Sciences, Texas A&M University, Houston, TX77030
| |
Collapse
|
2
|
Holze H, Talarmain L, Fennell KA, Lam EY, Dawson MA, Vassiliadis D. Analysis of synthetic cellular barcodes in the genome and transcriptome with BARtab and bartools. CELL REPORTS METHODS 2024; 4:100763. [PMID: 38670101 PMCID: PMC11133760 DOI: 10.1016/j.crmeth.2024.100763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/25/2024] [Accepted: 04/03/2024] [Indexed: 04/28/2024]
Abstract
Cellular barcoding is a lineage-tracing methodology that couples heritable synthetic barcodes to high-throughput sequencing, enabling the accurate tracing of cell lineages across a range of biological contexts. Recent studies have extended these methods by incorporating lineage information into single-cell or spatial transcriptomics readouts. Leveraging the rich biological information within these datasets requires dedicated computational tools for dataset pre-processing and analysis. Here, we present BARtab, a portable and scalable Nextflow pipeline, and bartools, an open-source R package, designed to provide an integrated end-to-end cellular barcoding analysis toolkit. BARtab and bartools contain methods to simplify the extraction, quality control, analysis, and visualization of lineage barcodes from population-level, single-cell, and spatial transcriptomics experiments. We showcase the utility of our integrated BARtab and bartools workflow via the analysis of exemplar bulk, single-cell, and spatial transcriptomics experiments containing cellular barcoding information.
Collapse
Affiliation(s)
- Henrietta Holze
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Laure Talarmain
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Katie A Fennell
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Enid Y Lam
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Mark A Dawson
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia; The University of Melbourne Centre for Cancer Research, The University of Melbourne, Melbourne, VIC 3000, Australia.
| | - Dane Vassiliadis
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3000, Australia.
| |
Collapse
|
3
|
Wang L, Dong W, Yin Z, Sheng J, Ezeana CF, Yang L, Yu X, Wong SSY, Wan Z, Danforth RL, Han K, Gao D, Wong STC. Charting Single Cell Lineage Dynamics and Mutation Networks via Homing CRISPR. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.05.574236. [PMID: 38260351 PMCID: PMC10802354 DOI: 10.1101/2024.01.05.574236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Single cell lineage tracing, essential for unraveling cellular dynamics in disease evolution is critical for developing targeted therapies. CRISPR-Cas9, known for inducing permanent and cumulative mutations, is a cornerstone in lineage tracing. The novel homing guide RNA (hgRNA) technology enhances this by enabling dynamic retargeting and facilitating ongoing genetic modifications. Charting these mutations, especially through successive hgRNA edits, poses a significant challenge. Our solution, LINEMAP, is a computational framework designed to trace and map these mutations with precision. LINEMAP meticulously discerns mutation alleles at single-cell resolution and maps their complex interrelationships through a mutation evolution network. By utilizing a Markov Process model, we can predict mutation transition probabilities, revealing potential mutational routes and pathways. Our reconstruction algorithm, anchored in the Markov model's attributes, reconstructs cellular lineage pathways, shedding light on the cell's evolutionary journey to the minutiae of single-cell division. Our findings reveal an intricate network of mutation evolution paired with a predictive Markov model, advancing our capability to reconstruct single-cell lineage via hgRNA. This has substantial implications for advancing our understanding of biological mechanisms and propelling medical research forward.
Collapse
Affiliation(s)
- Lin Wang
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Wenjuan Dong
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Zheng Yin
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
- Biostatistics and Bioinformatics Shared Resource, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Jianting Sheng
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Chika F. Ezeana
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Li Yang
- T.T. and W. F. Chao Center for BRAIN, Houston Methodist Research Institute, Houston, Texas 77030
| | - Xiaohui Yu
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | | | - Zhihao Wan
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Rebecca L. Danforth
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Kun Han
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
| | - Dingcheng Gao
- Department of Cell & Development Biology, Weill Cornell Medical College, New York, NY 10065
| | - Stephen T. C. Wong
- Department of System Medicine and Bioengineering, Houston Methodist Neal Cancer Center, Houston, Texas 77030
- Departments of Radiology, Pathology and Genomic Medicine, Houston Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030
| |
Collapse
|