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Integrated decoding hematopoiesis and leukemogenesis using single-cell sequencing and its medical implication
Single-cell RNA sequencing provides exciting opportunities to unbiasedly study hematopoiesis. However, our understanding of leukemogenesis was limited due to the high individual differences. Integrated analyses of hematopoiesis and leukemogenesis potentially provides new insights. Here we analyzed ~...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Singapore
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7788081/ https://www.ncbi.nlm.nih.gov/pubmed/33408321 http://dx.doi.org/10.1038/s41421-020-00223-4 |
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author | Qin, Pengfei Pang, Yakun Hou, Wenhong Fu, Ruiqing Zhang, Yingchi Wang, Xuefei Meng, Guofeng Liu, Qifa Zhu, Xiaofan Hong, Ni Cheng, Tao Jin, Wenfei |
author_facet | Qin, Pengfei Pang, Yakun Hou, Wenhong Fu, Ruiqing Zhang, Yingchi Wang, Xuefei Meng, Guofeng Liu, Qifa Zhu, Xiaofan Hong, Ni Cheng, Tao Jin, Wenfei |
author_sort | Qin, Pengfei |
collection | PubMed |
description | Single-cell RNA sequencing provides exciting opportunities to unbiasedly study hematopoiesis. However, our understanding of leukemogenesis was limited due to the high individual differences. Integrated analyses of hematopoiesis and leukemogenesis potentially provides new insights. Here we analyzed ~200,000 single-cell transcriptomes of bone marrow mononuclear cells (BMMCs) and its subsets from 23 clinical samples. We constructed a comprehensive cell atlas as hematopoietic reference. We developed counterpart composite index (CCI; available at GitHub: https://github.com/pengfeeei/cci) to search for the healthy counterpart of each leukemia cell subpopulation, by integrating multiple statistics to map leukemia cells onto reference hematopoietic cells. Interestingly, we found leukemia cell subpopulations from each patient had different healthy counterparts. Analysis showed the trajectories of leukemia cell subpopulations were similar to that of their healthy counterparts, indicating that developmental termination of leukemia initiating cells at different phases leads to different leukemia cell subpopulations thus explained the origin of leukemia heterogeneity. CCI further predicts leukemia subtypes, cellular heterogeneity, and cellular stemness of each leukemia patient. Analyses of leukemia patient at diagnosis, refractory, remission and relapse vividly presented dynamics of cell population during leukemia treatment. CCI analyses showed the healthy counterparts of relapsed leukemia cells were closer to the root of hematopoietic tree than that of other leukemia cells, although single-cell transcriptomic genetic variants and haplotype tracing analyses showed the relapsed leukemia cell were derived from an early minor leukemia cell population. In summary, this study developed a unified framework for understanding leukemogenesis with hematopoiesis reference, which provided novel biological and medical implication. |
format | Online Article Text |
id | pubmed-7788081 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-77880812021-01-14 Integrated decoding hematopoiesis and leukemogenesis using single-cell sequencing and its medical implication Qin, Pengfei Pang, Yakun Hou, Wenhong Fu, Ruiqing Zhang, Yingchi Wang, Xuefei Meng, Guofeng Liu, Qifa Zhu, Xiaofan Hong, Ni Cheng, Tao Jin, Wenfei Cell Discov Article Single-cell RNA sequencing provides exciting opportunities to unbiasedly study hematopoiesis. However, our understanding of leukemogenesis was limited due to the high individual differences. Integrated analyses of hematopoiesis and leukemogenesis potentially provides new insights. Here we analyzed ~200,000 single-cell transcriptomes of bone marrow mononuclear cells (BMMCs) and its subsets from 23 clinical samples. We constructed a comprehensive cell atlas as hematopoietic reference. We developed counterpart composite index (CCI; available at GitHub: https://github.com/pengfeeei/cci) to search for the healthy counterpart of each leukemia cell subpopulation, by integrating multiple statistics to map leukemia cells onto reference hematopoietic cells. Interestingly, we found leukemia cell subpopulations from each patient had different healthy counterparts. Analysis showed the trajectories of leukemia cell subpopulations were similar to that of their healthy counterparts, indicating that developmental termination of leukemia initiating cells at different phases leads to different leukemia cell subpopulations thus explained the origin of leukemia heterogeneity. CCI further predicts leukemia subtypes, cellular heterogeneity, and cellular stemness of each leukemia patient. Analyses of leukemia patient at diagnosis, refractory, remission and relapse vividly presented dynamics of cell population during leukemia treatment. CCI analyses showed the healthy counterparts of relapsed leukemia cells were closer to the root of hematopoietic tree than that of other leukemia cells, although single-cell transcriptomic genetic variants and haplotype tracing analyses showed the relapsed leukemia cell were derived from an early minor leukemia cell population. In summary, this study developed a unified framework for understanding leukemogenesis with hematopoiesis reference, which provided novel biological and medical implication. Springer Singapore 2021-01-05 /pmc/articles/PMC7788081/ /pubmed/33408321 http://dx.doi.org/10.1038/s41421-020-00223-4 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Qin, Pengfei Pang, Yakun Hou, Wenhong Fu, Ruiqing Zhang, Yingchi Wang, Xuefei Meng, Guofeng Liu, Qifa Zhu, Xiaofan Hong, Ni Cheng, Tao Jin, Wenfei Integrated decoding hematopoiesis and leukemogenesis using single-cell sequencing and its medical implication |
title | Integrated decoding hematopoiesis and leukemogenesis using single-cell sequencing and its medical implication |
title_full | Integrated decoding hematopoiesis and leukemogenesis using single-cell sequencing and its medical implication |
title_fullStr | Integrated decoding hematopoiesis and leukemogenesis using single-cell sequencing and its medical implication |
title_full_unstemmed | Integrated decoding hematopoiesis and leukemogenesis using single-cell sequencing and its medical implication |
title_short | Integrated decoding hematopoiesis and leukemogenesis using single-cell sequencing and its medical implication |
title_sort | integrated decoding hematopoiesis and leukemogenesis using single-cell sequencing and its medical implication |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7788081/ https://www.ncbi.nlm.nih.gov/pubmed/33408321 http://dx.doi.org/10.1038/s41421-020-00223-4 |
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