Inference of genomic lesions from single-cell RNA-seq in myeloma improves functional intraclonal and interclonal analysis
•scRNA-seq data can be used to infer the presence of the major cytogenetic alterations in MM.•Single-cell B-cell receptor profiling along with copy number and transcriptome analysis improves functional dissection of myeloma subclones. [Display omitted] Smoldering multiple myeloma (SMM) is an asympto...
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creator | Lazzaroni, Francesca Matera, Antonio Marella, Alessio Maeda, Akihiro Castellano, Giancarlo Marchetti, Alfredo Fabris, Sonia Pioggia, Stefania Silvestris, Ilaria Ronchetti, Domenica Lonati, Silvia Fabbiano, Giuseppina Traini, Valentina Taiana, Elisa Porretti, Laura Colombo, Federico De Magistris, Claudio Scopetti, Margherita Barbieri, Marzia Pettine, Loredana Torricelli, Federica Neri, Antonino Passamonti, Francesco Lionetti, Marta Da Vià, Matteo Claudio Bolli, Niccolò |
description | •scRNA-seq data can be used to infer the presence of the major cytogenetic alterations in MM.•Single-cell B-cell receptor profiling along with copy number and transcriptome analysis improves functional dissection of myeloma subclones.
[Display omitted]
Smoldering multiple myeloma (SMM) is an asymptomatic plasma cell (PC) neoplasm that may evolve with variable frequency into multiple myeloma (MM). SMM is initiated by chromosomal translocations involving the immunoglobulin heavy-chain locus or by hyperdiploidy and evolves through acquisition of additional genetic lesions. In this scenario, we aimed at establishing a reliable analysis pipeline to infer genomic lesions from transcriptomic analysis, by combining single-cell RNA sequencing (scRNA-seq) with B-cell receptor sequencing and copy number abnormality (CNA) analysis to identify clonal PCs at the genetic level along their specific transcriptional landscape. We profiled 20 465 bone marrow PCs derived from 5 patients with SMM/MM and unbiasedly identified clonal and polyclonal PCs. Hyperdiploidy, t(11;14), and t(6;14) were identified at the scRNA level by analysis of chimeric reads. Subclone functional analysis was improved by combining transcriptome with CNA analysis. As examples, we illustrate the different functional properties of a light-chain escape subclone in SMM and of different B-cell and PC subclones in a patient affected by Wäldenstrom macroglobulinemia and SMM. Overall, our data provide a proof of principle for inference of clinically relevant genotypic data from scRNA-seq, which in turn will refine functional annotation of the clonal architecture of PC dyscrasias. |
doi_str_mv | 10.1182/bloodadvances.2023012409 |
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[Display omitted]
Smoldering multiple myeloma (SMM) is an asymptomatic plasma cell (PC) neoplasm that may evolve with variable frequency into multiple myeloma (MM). SMM is initiated by chromosomal translocations involving the immunoglobulin heavy-chain locus or by hyperdiploidy and evolves through acquisition of additional genetic lesions. In this scenario, we aimed at establishing a reliable analysis pipeline to infer genomic lesions from transcriptomic analysis, by combining single-cell RNA sequencing (scRNA-seq) with B-cell receptor sequencing and copy number abnormality (CNA) analysis to identify clonal PCs at the genetic level along their specific transcriptional landscape. We profiled 20 465 bone marrow PCs derived from 5 patients with SMM/MM and unbiasedly identified clonal and polyclonal PCs. Hyperdiploidy, t(11;14), and t(6;14) were identified at the scRNA level by analysis of chimeric reads. Subclone functional analysis was improved by combining transcriptome with CNA analysis. As examples, we illustrate the different functional properties of a light-chain escape subclone in SMM and of different B-cell and PC subclones in a patient affected by Wäldenstrom macroglobulinemia and SMM. Overall, our data provide a proof of principle for inference of clinically relevant genotypic data from scRNA-seq, which in turn will refine functional annotation of the clonal architecture of PC dyscrasias.</description><identifier>ISSN: 2473-9529</identifier><identifier>ISSN: 2473-9537</identifier><identifier>EISSN: 2473-9537</identifier><identifier>DOI: 10.1182/bloodadvances.2023012409</identifier><identifier>PMID: 38830132</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Gene Expression Profiling ; Genomics - methods ; Humans ; Lymphoid Neoplasia ; Multiple Myeloma - genetics ; Multiple Myeloma - pathology ; Plasma Cells - metabolism ; Plasma Cells - pathology ; RNA-Seq ; Single-Cell Analysis - methods ; Single-Cell Gene Expression Analysis</subject><ispartof>Blood advances, 2024-08, Vol.8 (15), p.3972-3984</ispartof><rights>2024 The American Society of Hematology</rights><rights>2024 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.</rights><rights>2024 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved. 2024 The American Society of Hematology</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c355t-7a12b3bef3968a2c776b8178da4d585bcc430b120594f8adf8c711d6cbd7103e3</cites><orcidid>0000-0002-4824-3445 ; 0009-0001-3034-9874 ; 0000-0003-1341-0503 ; 0000-0001-8100-4262 ; 0009-0003-9193-5062 ; 0009-0007-6583-6836 ; 0000-0003-4480-3481 ; 0000-0001-9553-2082 ; 0000-0001-5767-7846 ; 0000-0002-1018-5139</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11331727/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11331727/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38830132$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lazzaroni, Francesca</creatorcontrib><creatorcontrib>Matera, Antonio</creatorcontrib><creatorcontrib>Marella, Alessio</creatorcontrib><creatorcontrib>Maeda, Akihiro</creatorcontrib><creatorcontrib>Castellano, Giancarlo</creatorcontrib><creatorcontrib>Marchetti, Alfredo</creatorcontrib><creatorcontrib>Fabris, Sonia</creatorcontrib><creatorcontrib>Pioggia, Stefania</creatorcontrib><creatorcontrib>Silvestris, Ilaria</creatorcontrib><creatorcontrib>Ronchetti, Domenica</creatorcontrib><creatorcontrib>Lonati, Silvia</creatorcontrib><creatorcontrib>Fabbiano, Giuseppina</creatorcontrib><creatorcontrib>Traini, Valentina</creatorcontrib><creatorcontrib>Taiana, Elisa</creatorcontrib><creatorcontrib>Porretti, Laura</creatorcontrib><creatorcontrib>Colombo, Federico</creatorcontrib><creatorcontrib>De Magistris, Claudio</creatorcontrib><creatorcontrib>Scopetti, Margherita</creatorcontrib><creatorcontrib>Barbieri, Marzia</creatorcontrib><creatorcontrib>Pettine, Loredana</creatorcontrib><creatorcontrib>Torricelli, Federica</creatorcontrib><creatorcontrib>Neri, Antonino</creatorcontrib><creatorcontrib>Passamonti, Francesco</creatorcontrib><creatorcontrib>Lionetti, Marta</creatorcontrib><creatorcontrib>Da Vià, Matteo Claudio</creatorcontrib><creatorcontrib>Bolli, Niccolò</creatorcontrib><title>Inference of genomic lesions from single-cell RNA-seq in myeloma improves functional intraclonal and interclonal analysis</title><title>Blood advances</title><addtitle>Blood Adv</addtitle><description>•scRNA-seq data can be used to infer the presence of the major cytogenetic alterations in MM.•Single-cell B-cell receptor profiling along with copy number and transcriptome analysis improves functional dissection of myeloma subclones.
[Display omitted]
Smoldering multiple myeloma (SMM) is an asymptomatic plasma cell (PC) neoplasm that may evolve with variable frequency into multiple myeloma (MM). SMM is initiated by chromosomal translocations involving the immunoglobulin heavy-chain locus or by hyperdiploidy and evolves through acquisition of additional genetic lesions. In this scenario, we aimed at establishing a reliable analysis pipeline to infer genomic lesions from transcriptomic analysis, by combining single-cell RNA sequencing (scRNA-seq) with B-cell receptor sequencing and copy number abnormality (CNA) analysis to identify clonal PCs at the genetic level along their specific transcriptional landscape. We profiled 20 465 bone marrow PCs derived from 5 patients with SMM/MM and unbiasedly identified clonal and polyclonal PCs. Hyperdiploidy, t(11;14), and t(6;14) were identified at the scRNA level by analysis of chimeric reads. Subclone functional analysis was improved by combining transcriptome with CNA analysis. As examples, we illustrate the different functional properties of a light-chain escape subclone in SMM and of different B-cell and PC subclones in a patient affected by Wäldenstrom macroglobulinemia and SMM. Overall, our data provide a proof of principle for inference of clinically relevant genotypic data from scRNA-seq, which in turn will refine functional annotation of the clonal architecture of PC dyscrasias.</description><subject>Gene Expression Profiling</subject><subject>Genomics - methods</subject><subject>Humans</subject><subject>Lymphoid Neoplasia</subject><subject>Multiple Myeloma - genetics</subject><subject>Multiple Myeloma - pathology</subject><subject>Plasma Cells - metabolism</subject><subject>Plasma Cells - pathology</subject><subject>RNA-Seq</subject><subject>Single-Cell Analysis - methods</subject><subject>Single-Cell Gene Expression Analysis</subject><issn>2473-9529</issn><issn>2473-9537</issn><issn>2473-9537</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFUUtv1DAQtlARrUr_AvKxlxQ_4tg5VW1VYKUKJARny7Eni5Fjb-3sSvvv67BloaeeZkbzPUbzIYQpuaJUsY9DSMkZtzPRQrlihHFCWUv6N-iMtZI3veDy5Niz_hRdlPKbEEJlx0XP3qFTrlQlcXaG9qs4QoYqhdOI1xDT5C0OUHyKBY85Tbj4uA7QWAgBf_960xR4xD7iaQ8hTQb7aZPTDip4G-1caSbU9ZyNDX96E90yQz7OJuyLL-_R29GEAhfP9Rz9_HT_4-5L8_Dt8-ru5qGxXIi5kYaygQ8w8r5Thlkpu0FRqZxpnVBisLblZKCMiL4dlXGjspJS19nBSUo48HN0fdDdbIcJnIXltqA32U8m73UyXr_cRP9Lr9NOU8o5lUxWhctnhZwet1BmPfmyvMNESNuiOelaoUgvRIWqA9TmVEqG8ehDiV7S0y_S0__Sq9QP_995JP7NqgJuDwCo39p5yLpYvyTnfAY7a5f86y5PaJq0fg</recordid><startdate>20240813</startdate><enddate>20240813</enddate><creator>Lazzaroni, Francesca</creator><creator>Matera, Antonio</creator><creator>Marella, Alessio</creator><creator>Maeda, Akihiro</creator><creator>Castellano, Giancarlo</creator><creator>Marchetti, Alfredo</creator><creator>Fabris, Sonia</creator><creator>Pioggia, Stefania</creator><creator>Silvestris, Ilaria</creator><creator>Ronchetti, Domenica</creator><creator>Lonati, Silvia</creator><creator>Fabbiano, Giuseppina</creator><creator>Traini, Valentina</creator><creator>Taiana, Elisa</creator><creator>Porretti, Laura</creator><creator>Colombo, Federico</creator><creator>De Magistris, Claudio</creator><creator>Scopetti, Margherita</creator><creator>Barbieri, Marzia</creator><creator>Pettine, Loredana</creator><creator>Torricelli, Federica</creator><creator>Neri, Antonino</creator><creator>Passamonti, Francesco</creator><creator>Lionetti, Marta</creator><creator>Da Vià, Matteo Claudio</creator><creator>Bolli, Niccolò</creator><general>Elsevier Inc</general><general>The American Society of Hematology</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-4824-3445</orcidid><orcidid>https://orcid.org/0009-0001-3034-9874</orcidid><orcidid>https://orcid.org/0000-0003-1341-0503</orcidid><orcidid>https://orcid.org/0000-0001-8100-4262</orcidid><orcidid>https://orcid.org/0009-0003-9193-5062</orcidid><orcidid>https://orcid.org/0009-0007-6583-6836</orcidid><orcidid>https://orcid.org/0000-0003-4480-3481</orcidid><orcidid>https://orcid.org/0000-0001-9553-2082</orcidid><orcidid>https://orcid.org/0000-0001-5767-7846</orcidid><orcidid>https://orcid.org/0000-0002-1018-5139</orcidid></search><sort><creationdate>20240813</creationdate><title>Inference of genomic lesions from single-cell RNA-seq in myeloma improves functional intraclonal and interclonal analysis</title><author>Lazzaroni, Francesca ; Matera, Antonio ; Marella, Alessio ; Maeda, Akihiro ; Castellano, Giancarlo ; Marchetti, Alfredo ; Fabris, Sonia ; Pioggia, Stefania ; Silvestris, Ilaria ; Ronchetti, Domenica ; Lonati, Silvia ; Fabbiano, Giuseppina ; Traini, Valentina ; Taiana, Elisa ; Porretti, Laura ; Colombo, Federico ; De Magistris, Claudio ; Scopetti, Margherita ; Barbieri, Marzia ; Pettine, Loredana ; Torricelli, Federica ; Neri, Antonino ; Passamonti, Francesco ; Lionetti, Marta ; Da Vià, Matteo Claudio ; Bolli, Niccolò</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c355t-7a12b3bef3968a2c776b8178da4d585bcc430b120594f8adf8c711d6cbd7103e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Gene Expression Profiling</topic><topic>Genomics - methods</topic><topic>Humans</topic><topic>Lymphoid Neoplasia</topic><topic>Multiple Myeloma - genetics</topic><topic>Multiple Myeloma - pathology</topic><topic>Plasma Cells - metabolism</topic><topic>Plasma Cells - pathology</topic><topic>RNA-Seq</topic><topic>Single-Cell Analysis - methods</topic><topic>Single-Cell Gene Expression Analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lazzaroni, Francesca</creatorcontrib><creatorcontrib>Matera, Antonio</creatorcontrib><creatorcontrib>Marella, Alessio</creatorcontrib><creatorcontrib>Maeda, Akihiro</creatorcontrib><creatorcontrib>Castellano, Giancarlo</creatorcontrib><creatorcontrib>Marchetti, Alfredo</creatorcontrib><creatorcontrib>Fabris, Sonia</creatorcontrib><creatorcontrib>Pioggia, Stefania</creatorcontrib><creatorcontrib>Silvestris, Ilaria</creatorcontrib><creatorcontrib>Ronchetti, Domenica</creatorcontrib><creatorcontrib>Lonati, Silvia</creatorcontrib><creatorcontrib>Fabbiano, Giuseppina</creatorcontrib><creatorcontrib>Traini, Valentina</creatorcontrib><creatorcontrib>Taiana, Elisa</creatorcontrib><creatorcontrib>Porretti, Laura</creatorcontrib><creatorcontrib>Colombo, Federico</creatorcontrib><creatorcontrib>De Magistris, Claudio</creatorcontrib><creatorcontrib>Scopetti, Margherita</creatorcontrib><creatorcontrib>Barbieri, Marzia</creatorcontrib><creatorcontrib>Pettine, Loredana</creatorcontrib><creatorcontrib>Torricelli, Federica</creatorcontrib><creatorcontrib>Neri, Antonino</creatorcontrib><creatorcontrib>Passamonti, Francesco</creatorcontrib><creatorcontrib>Lionetti, Marta</creatorcontrib><creatorcontrib>Da Vià, Matteo Claudio</creatorcontrib><creatorcontrib>Bolli, Niccolò</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Blood advances</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lazzaroni, Francesca</au><au>Matera, Antonio</au><au>Marella, Alessio</au><au>Maeda, Akihiro</au><au>Castellano, Giancarlo</au><au>Marchetti, Alfredo</au><au>Fabris, Sonia</au><au>Pioggia, Stefania</au><au>Silvestris, Ilaria</au><au>Ronchetti, Domenica</au><au>Lonati, Silvia</au><au>Fabbiano, Giuseppina</au><au>Traini, Valentina</au><au>Taiana, Elisa</au><au>Porretti, Laura</au><au>Colombo, Federico</au><au>De Magistris, Claudio</au><au>Scopetti, Margherita</au><au>Barbieri, Marzia</au><au>Pettine, Loredana</au><au>Torricelli, Federica</au><au>Neri, Antonino</au><au>Passamonti, Francesco</au><au>Lionetti, Marta</au><au>Da Vià, Matteo Claudio</au><au>Bolli, Niccolò</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Inference of genomic lesions from single-cell RNA-seq in myeloma improves functional intraclonal and interclonal analysis</atitle><jtitle>Blood advances</jtitle><addtitle>Blood Adv</addtitle><date>2024-08-13</date><risdate>2024</risdate><volume>8</volume><issue>15</issue><spage>3972</spage><epage>3984</epage><pages>3972-3984</pages><issn>2473-9529</issn><issn>2473-9537</issn><eissn>2473-9537</eissn><abstract>•scRNA-seq data can be used to infer the presence of the major cytogenetic alterations in MM.•Single-cell B-cell receptor profiling along with copy number and transcriptome analysis improves functional dissection of myeloma subclones.
[Display omitted]
Smoldering multiple myeloma (SMM) is an asymptomatic plasma cell (PC) neoplasm that may evolve with variable frequency into multiple myeloma (MM). SMM is initiated by chromosomal translocations involving the immunoglobulin heavy-chain locus or by hyperdiploidy and evolves through acquisition of additional genetic lesions. In this scenario, we aimed at establishing a reliable analysis pipeline to infer genomic lesions from transcriptomic analysis, by combining single-cell RNA sequencing (scRNA-seq) with B-cell receptor sequencing and copy number abnormality (CNA) analysis to identify clonal PCs at the genetic level along their specific transcriptional landscape. We profiled 20 465 bone marrow PCs derived from 5 patients with SMM/MM and unbiasedly identified clonal and polyclonal PCs. Hyperdiploidy, t(11;14), and t(6;14) were identified at the scRNA level by analysis of chimeric reads. Subclone functional analysis was improved by combining transcriptome with CNA analysis. As examples, we illustrate the different functional properties of a light-chain escape subclone in SMM and of different B-cell and PC subclones in a patient affected by Wäldenstrom macroglobulinemia and SMM. Overall, our data provide a proof of principle for inference of clinically relevant genotypic data from scRNA-seq, which in turn will refine functional annotation of the clonal architecture of PC dyscrasias.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>38830132</pmid><doi>10.1182/bloodadvances.2023012409</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-4824-3445</orcidid><orcidid>https://orcid.org/0009-0001-3034-9874</orcidid><orcidid>https://orcid.org/0000-0003-1341-0503</orcidid><orcidid>https://orcid.org/0000-0001-8100-4262</orcidid><orcidid>https://orcid.org/0009-0003-9193-5062</orcidid><orcidid>https://orcid.org/0009-0007-6583-6836</orcidid><orcidid>https://orcid.org/0000-0003-4480-3481</orcidid><orcidid>https://orcid.org/0000-0001-9553-2082</orcidid><orcidid>https://orcid.org/0000-0001-5767-7846</orcidid><orcidid>https://orcid.org/0000-0002-1018-5139</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Gene Expression Profiling Genomics - methods Humans Lymphoid Neoplasia Multiple Myeloma - genetics Multiple Myeloma - pathology Plasma Cells - metabolism Plasma Cells - pathology RNA-Seq Single-Cell Analysis - methods Single-Cell Gene Expression Analysis |
title | Inference of genomic lesions from single-cell RNA-seq in myeloma improves functional intraclonal and interclonal analysis |
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