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Kurt S, Chen M, Toosi H, Chen X, Engblom C, Mold J, Hartman J, Lagergren J. CopyVAE: a variational autoencoder-based approach for copy number variation inference using single-cell transcriptomics. Bioinformatics 2024; 40:btae284. [PMID: 38676578 PMCID: PMC11087824 DOI: 10.1093/bioinformatics/btae284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 03/06/2024] [Accepted: 04/25/2024] [Indexed: 04/29/2024] Open
Abstract
MOTIVATION Copy number variations (CNVs) are common genetic alterations in tumour cells. The delineation of CNVs holds promise for enhancing our comprehension of cancer progression. Moreover, accurate inference of CNVs from single-cell sequencing data is essential for unravelling intratumoral heterogeneity. However, existing inference methods face limitations in resolution and sensitivity. RESULTS To address these challenges, we present CopyVAE, a deep learning framework based on a variational autoencoder architecture. Through experiments, we demonstrated that CopyVAE can accurately and reliably detect CNVs from data obtained using single-cell RNA sequencing. CopyVAE surpasses existing methods in terms of sensitivity and specificity. We also discussed CopyVAE's potential to advance our understanding of genetic alterations and their impact on disease advancement. AVAILABILITY AND IMPLEMENTATION CopyVAE is implemented and freely available under MIT license at https://github.com/kurtsemih/copyVAE.
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Affiliation(s)
- Semih Kurt
- School of EECS and SciLifeLab, KTH Royal Institute of Technology, Stockholm, 100 44, Sweden
| | - Mandi Chen
- School of EECS and SciLifeLab, KTH Royal Institute of Technology, Stockholm, 100 44, Sweden
| | - Hosein Toosi
- School of EECS and SciLifeLab, KTH Royal Institute of Technology, Stockholm, 100 44, Sweden
| | - Xinsong Chen
- Department of Oncology and Pathology, Karolinska Institutet, Solna, 171 77, Sweden
| | - Camilla Engblom
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, 171 77, Sweden
| | - Jeff Mold
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, 171 77, Sweden
| | - Johan Hartman
- Department of Oncology and Pathology, Karolinska Institutet, Solna, 171 77, Sweden
- Department of Clinical Pathology and Cytology, Karolinska University Laboratory, Solna, 171 76, Sweden
| | - Jens Lagergren
- School of EECS and SciLifeLab, KTH Royal Institute of Technology, Stockholm, 100 44, Sweden
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Mold JE, Weissman MH, Ratz M, Hagemann-Jensen M, Hård J, Eriksson CJ, Toosi H, Berghenstråhle J, Ziegenhain C, von Berlin L, Martin M, Blom K, Lagergren J, Lundeberg J, Sandberg R, Michaëlsson J, Frisén J. Clonally heritable gene expression imparts a layer of diversity within cell types. Cell Syst 2024; 15:149-165.e10. [PMID: 38340731 DOI: 10.1016/j.cels.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 05/25/2023] [Accepted: 01/18/2024] [Indexed: 02/12/2024]
Abstract
Cell types can be classified according to shared patterns of transcription. Non-genetic variability among individual cells of the same type has been ascribed to stochastic transcriptional bursting and transient cell states. Using high-coverage single-cell RNA profiling, we asked whether long-term, heritable differences in gene expression can impart diversity within cells of the same type. Studying clonal human lymphocytes and mouse brain cells, we uncovered a vast diversity of heritable gene expression patterns among different clones of cells of the same type in vivo. We combined chromatin accessibility and RNA profiling on different lymphocyte clones to reveal thousands of regulatory regions exhibiting interclonal variation, which could be directly linked to interclonal variation in gene expression. Our findings identify a source of cellular diversity, which may have important implications for how cellular populations are shaped by selective processes in development, aging, and disease. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Jeff E Mold
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Martin H Weissman
- Mathematics Department, University of California, Santa Cruz, CA, USA.
| | - Michael Ratz
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden; SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | | | - Joanna Hård
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Carl-Johan Eriksson
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Hosein Toosi
- SciLifeLab, Computational Science and Technology Department, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Joseph Berghenstråhle
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Christoph Ziegenhain
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Leonie von Berlin
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Marcel Martin
- Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, SciLifeLab, Stockholm University, Stockholm, Sweden
| | - Kim Blom
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institute, Stockholm, Sweden
| | - Jens Lagergren
- SciLifeLab, Computational Science and Technology Department, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Joakim Lundeberg
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Rickard Sandberg
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Jakob Michaëlsson
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institute, Stockholm, Sweden.
| | - Jonas Frisén
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
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Engblom C, Thrane K, Lin Q, Andersson A, Toosi H, Chen X, Steiner E, Lu C, Mantovani G, Hagemann-Jensen M, Saarenpää S, Jangard M, Saez-Rodriguez J, Michaëlsson J, Hartman J, Lagergren J, Mold JE, Lundeberg J, Frisén J. Spatial transcriptomics of B cell and T cell receptors reveals lymphocyte clonal dynamics. Science 2023; 382:eadf8486. [PMID: 38060664 DOI: 10.1126/science.adf8486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 10/23/2023] [Indexed: 12/18/2023]
Abstract
The spatial distribution of lymphocyte clones within tissues is critical to their development, selection, and expansion. We have developed spatial transcriptomics of variable, diversity, and joining (VDJ) sequences (Spatial VDJ), a method that maps B cell and T cell receptor sequences in human tissue sections. Spatial VDJ captures lymphocyte clones that match canonical B and T cell distributions and amplifies clonal sequences confirmed by orthogonal methods. We found spatial congruency between paired receptor chains, developed a computational framework to predict receptor pairs, and linked the expansion of distinct B cell clones to different tumor-associated gene expression programs. Spatial VDJ delineates B cell clonal diversity and lineage trajectories within their anatomical niche. Thus, Spatial VDJ captures lymphocyte spatial clonal architecture across tissues, providing a platform to harness clonal sequences for therapy.
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Affiliation(s)
- Camilla Engblom
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Kim Thrane
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Qirong Lin
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Alma Andersson
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Hosein Toosi
- SciLifeLab, Computational Science and Technology department, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Xinsong Chen
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Embla Steiner
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Chang Lu
- Heidelberg University, Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | - Giulia Mantovani
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | | | - Sami Saarenpää
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Mattias Jangard
- ENT Unit, Sophiahemmet University Research Laboratory and Sophiahemmet Hospital, Stockholm, Sweden
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | - Jakob Michaëlsson
- Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
| | - Johan Hartman
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
| | - Jens Lagergren
- SciLifeLab, Computational Science and Technology department, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jeff E Mold
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Joakim Lundeberg
- SciLifeLab, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jonas Frisén
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
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Geras A, Darvish Shafighi S, Domżał K, Filipiuk I, Rączkowska A, Szymczak P, Toosi H, Kaczmarek L, Koperski Ł, Lagergren J, Nowis D, Szczurek E. Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data. Genome Biol 2023; 24:120. [PMID: 37198601 PMCID: PMC10190053 DOI: 10.1186/s13059-023-02951-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 04/21/2023] [Indexed: 05/19/2023] Open
Abstract
Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells. Therefore, the observed signal comes from mixtures of cells of different types. Here, we propose an innovative probabilistic model, Celloscope, that utilizes established prior knowledge on marker genes for cell type deconvolution from spatial transcriptomics data. Celloscope outperforms other methods on simulated data, successfully indicates known brain structures and spatially distinguishes between inhibitory and excitatory neuron types based in mouse brain tissue, and dissects large heterogeneity of immune infiltrate composition in prostate gland tissue.
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Affiliation(s)
- Agnieszka Geras
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Shadi Darvish Shafighi
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative - UMR, Paris, France
| | - Kacper Domżał
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Igor Filipiuk
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Alicja Rączkowska
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Paulina Szymczak
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
| | - Hosein Toosi
- KTH Royal Institute of Technology, Stockholm, Sweden
| | - Leszek Kaczmarek
- BRAINCITY, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland
| | - Łukasz Koperski
- Department of Pathology, Medical University of Warsaw, Warsaw, Poland
| | | | - Dominika Nowis
- Laboratory of Experimental Medicine, Medical University of Warsaw, Warsaw, Poland
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland.
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5
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Jun SH, Toosi H, Mold J, Engblom C, Chen X, O'Flanagan C, Hagemann-Jensen M, Sandberg R, Aparicio S, Hartman J, Roth A, Lagergren J. Reconstructing clonal tree for phylo-phenotypic characterization of cancer using single-cell transcriptomics. Nat Commun 2023; 14:982. [PMID: 36813776 PMCID: PMC9946941 DOI: 10.1038/s41467-023-36202-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 01/20/2023] [Indexed: 02/24/2023] Open
Abstract
Functional characterization of the cancer clones can shed light on the evolutionary mechanisms driving cancer's proliferation and relapse mechanisms. Single-cell RNA sequencing data provide grounds for understanding the functional state of cancer as a whole; however, much research remains to identify and reconstruct clonal relationships toward characterizing the changes in functions of individual clones. We present PhylEx that integrates bulk genomics data with co-occurrences of mutations from single-cell RNA sequencing data to reconstruct high-fidelity clonal trees. We evaluate PhylEx on synthetic and well-characterized high-grade serous ovarian cancer cell line datasets. PhylEx outperforms the state-of-the-art methods both when comparing capacity for clonal tree reconstruction and for identifying clones. We analyze high-grade serous ovarian cancer and breast cancer data to show that PhylEx exploits clonal expression profiles beyond what is possible with expression-based clustering methods and clear the way for accurate inference of clonal trees and robust phylo-phenotypic analysis of cancer.
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Affiliation(s)
- Seong-Hwan Jun
- SciLifeLab, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden.,Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, USA
| | - Hosein Toosi
- SciLifeLab, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jeff Mold
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Camilla Engblom
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Xinsong Chen
- Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden
| | - Ciara O'Flanagan
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | | | - Rickard Sandberg
- Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
| | - Samuel Aparicio
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada.,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
| | - Johan Hartman
- Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden.,Department of Clinical Pathology and Cytology, Karolinska University Laboratory, Stockholm, Sweden
| | - Andrew Roth
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada. .,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada. .,Department of Computer Science, University of British Columbia, Vancouver, Canada.
| | - Jens Lagergren
- SciLifeLab, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden.
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Abstract
BACKGROUND Intra-tumor heterogeneity is known to contribute to cancer complexity and drug resistance. Understanding the number of distinct subclones and the evolutionary relationships between them is scientifically and clinically very important and still a challenging problem. RESULTS In this paper, we present BAMSE (BAyesian Model Selection for tumor Evolution), a new probabilistic method for inferring subclonal history and lineage tree reconstruction of heterogeneous tumor samples. BAMSE uses somatic mutation read counts as input and can leverage multiple tumor samples accurately and efficiently. In the first step, possible clusterings of mutations into subclones are scored and a user defined number are selected for further analysis. In the next step, for each of these candidates, a list of trees describing the evolutionary relationships between the subclones is generated. These trees are sorted by their posterior probability. The posterior probability is calculated using a Bayesian model that integrates prior belief about the number of subclones, the composition of the tumor and the process of subclonal evolution. BAMSE also takes the sequencing error into account. We benchmarked BAMSE against state of the art software using simulated datasets. CONCLUSIONS In this work we developed a flexible and fast software to reconstruct the history of a tumor's subclonal evolution using somatic mutation read counts across multiple samples. BAMSE software is implemented in Python and is available open source under GNU GLPv3 at https://github.com/HoseinT/BAMSE .
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Affiliation(s)
- Hosein Toosi
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Moeini
- School of Engineering Sciences, College of Engineering, University of Tehran, Tehran, Iran.
| | - Iman Hajirasouliha
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA. .,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA. .,Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA. .,The Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
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Abstract
The reconstruction of cancer phylogeny trees and quantifying the evolution of the disease is a challenging task. LICHeE and BAMSE are two computational tools designed and implemented recently for this purpose. They both utilize estimated variant allele fraction of somatic mutations across multiple samples to infer the most likely cancer phylogenies. This unit provides extensive guidelines for installing and running both LICHeE and BAMSE. © 2018 by John Wiley & Sons, Inc.
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Affiliation(s)
- Camir Ricketts
- Tri-I Computational Biology & Medicine Graduate Program, Cornell University, New York, New York
- Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Victoria Popic
- Illumina Inc. Illumina Mission Bay, San Francisco, California
| | - Hosein Toosi
- Department of Bioinformatics, Institute for Biochemistry and Biophysics, University of Tehran, Iran
| | - Iman Hajirasouliha
- Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
- Englander Institute for Precision Medicine, The Meyer Cancer Center, Weill Cornell Medicine, New York
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