BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids
Summary: Our machine-learning framework, brain and organoid manifold alignment (BOMA), first performs a global alignment of developmental gene expression data between brains and organoids. It then applies manifold learning to locally refine the alignment, revealing conserved and specific development...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Cell Reports: Methods |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667237523000206 |
| _version_ | 1828001686748135424 |
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| author | Chenfeng He Noah Cohen Kalafut Soraya O. Sandoval Ryan Risgaard Carissa L. Sirois Chen Yang Saniya Khullar Marin Suzuki Xiang Huang Qiang Chang Xinyu Zhao Andre M.M. Sousa Daifeng Wang |
| author_facet | Chenfeng He Noah Cohen Kalafut Soraya O. Sandoval Ryan Risgaard Carissa L. Sirois Chen Yang Saniya Khullar Marin Suzuki Xiang Huang Qiang Chang Xinyu Zhao Andre M.M. Sousa Daifeng Wang |
| author_sort | Chenfeng He |
| collection | DOAJ |
| description | Summary: Our machine-learning framework, brain and organoid manifold alignment (BOMA), first performs a global alignment of developmental gene expression data between brains and organoids. It then applies manifold learning to locally refine the alignment, revealing conserved and specific developmental trajectories across brains and organoids. Using BOMA, we found that human cortical organoids better align with certain brain cortical regions than with other non-cortical regions, implying organoid-preserved developmental gene expression programs specific to brain regions. Additionally, our alignment of non-human primate and human brains reveals highly conserved gene expression around birth. Also, we integrated and analyzed developmental single-cell RNA sequencing (scRNA-seq) data of human brains and organoids, showing conserved and specific cell trajectories and clusters. Further identification of expressed genes of such clusters and enrichment analyses reveal brain- or organoid-specific developmental functions and pathways. Finally, we experimentally validated important specific expressed genes through the use of immunofluorescence. BOMA is open-source available as a web tool for community use. Motivation: Organoids have become valuable models for understanding cellular and molecular mechanisms in human development, including development of brains. However, whether developmental gene expression programs are preserved between human organoids and brains, especially in specific cell types, remains unclear. Importantly, there is a lack of effective computational approaches for comparative data analyses between organoids and developing human brains. To address this, we developed a machine-learning framework for comparative gene expression analysis of brains and organoids to identify conserved and specific developmental trajectories as well as developmentally expressed genes and functions, especially at cellular resolution. |
| first_indexed | 2024-04-10T06:32:48Z |
| format | Article |
| id | doaj.art-494be02e96414fa5a14749b2ddcb4ea7 |
| institution | Directory Open Access Journal |
| issn | 2667-2375 |
| language | English |
| last_indexed | 2024-04-10T06:32:48Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cell Reports: Methods |
| spelling | doaj.art-494be02e96414fa5a14749b2ddcb4ea72023-03-01T04:33:23ZengElsevierCell Reports: Methods2667-23752023-02-0132100409BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoidsChenfeng He0Noah Cohen Kalafut1Soraya O. Sandoval2Ryan Risgaard3Carissa L. Sirois4Chen Yang5Saniya Khullar6Marin Suzuki7Xiang Huang8Qiang Chang9Xinyu Zhao10Andre M.M. Sousa11Daifeng Wang12Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, Madison, WI, USAWaisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USAWaisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Neuroscience, University of Wisconsin-Madison, Madison, WI, USAWaisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Neuroscience, University of Wisconsin-Madison, Madison, WI, USAWaisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Neuroscience, University of Wisconsin-Madison, Madison, WI, USADepartment of Mathematics, University of Wisconsin-Madison, Madison, WI, USADepartment of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, Madison, WI, USADepartment of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USAWaisman Center, University of Wisconsin-Madison, Madison, WI, USAWaisman Center, University of Wisconsin-Madison, Madison, WI, USA; Departments of Medical Genetics and Neurology, University of Wisconsin-Madison, Madison, WI, USAWaisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Neuroscience, University of Wisconsin-Madison, Madison, WI, USAWaisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Neuroscience, University of Wisconsin-Madison, Madison, WI, USADepartment of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA; Corresponding authorSummary: Our machine-learning framework, brain and organoid manifold alignment (BOMA), first performs a global alignment of developmental gene expression data between brains and organoids. It then applies manifold learning to locally refine the alignment, revealing conserved and specific developmental trajectories across brains and organoids. Using BOMA, we found that human cortical organoids better align with certain brain cortical regions than with other non-cortical regions, implying organoid-preserved developmental gene expression programs specific to brain regions. Additionally, our alignment of non-human primate and human brains reveals highly conserved gene expression around birth. Also, we integrated and analyzed developmental single-cell RNA sequencing (scRNA-seq) data of human brains and organoids, showing conserved and specific cell trajectories and clusters. Further identification of expressed genes of such clusters and enrichment analyses reveal brain- or organoid-specific developmental functions and pathways. Finally, we experimentally validated important specific expressed genes through the use of immunofluorescence. BOMA is open-source available as a web tool for community use. Motivation: Organoids have become valuable models for understanding cellular and molecular mechanisms in human development, including development of brains. However, whether developmental gene expression programs are preserved between human organoids and brains, especially in specific cell types, remains unclear. Importantly, there is a lack of effective computational approaches for comparative data analyses between organoids and developing human brains. To address this, we developed a machine-learning framework for comparative gene expression analysis of brains and organoids to identify conserved and specific developmental trajectories as well as developmentally expressed genes and functions, especially at cellular resolution.http://www.sciencedirect.com/science/article/pii/S2667237523000206CP: Stem cellCP: Systems biology |
| spellingShingle | Chenfeng He Noah Cohen Kalafut Soraya O. Sandoval Ryan Risgaard Carissa L. Sirois Chen Yang Saniya Khullar Marin Suzuki Xiang Huang Qiang Chang Xinyu Zhao Andre M.M. Sousa Daifeng Wang BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids Cell Reports: Methods CP: Stem cell CP: Systems biology |
| title | BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids |
| title_full | BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids |
| title_fullStr | BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids |
| title_full_unstemmed | BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids |
| title_short | BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids |
| title_sort | boma a machine learning framework for comparative gene expression analysis across brains and organoids |
| topic | CP: Stem cell CP: Systems biology |
| url | http://www.sciencedirect.com/science/article/pii/S2667237523000206 |
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