File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Discrete single-cell microRNA analysis for phenotyping the heterogeneity of acute myeloid leukemia

TitleDiscrete single-cell microRNA analysis for phenotyping the heterogeneity of acute myeloid leukemia
Authors
KeywordsAcute myeloid leukemia
AML subtype
Cancer prognosis
Cellular heterogeneity
miRNA profiling
Nanomedicine
Issue Date25-Oct-2022
PublisherElsevier
Citation
Biomaterials, 2022, v. 291 How to Cite?
Abstract

Acute myeloid leukemia (AML) is a highly heterogenous cancer in hematopoiesis, and its subtype specification is greatly important in the clinical practice for AML diagnosis and prognosis. Increasing evidence has shown the association between microRNA (miRNA) phenotype and AML therapeutic outcomes, emphasizing the need for novel techniques for convenient, sensitive, and efficient miRNA profiling in clinical practices. Here, we describe a nanoneedle-based discrete single-cell microRNA profiling technique for multiplexed phenotyping of AML heterogeneity without the requirement of sequencing or polymerase chain reaction (PCR). In virtue of a biochip-based and non-destructive nature of the assay, the expression of nine miRNAs in large number of living AML cells can be simultaneously analyzed with discrete single-cell level information, thus providing a proof-of-concept demonstration of an AML subtype classifier based on the multidimensional miRNA data. We showed successful analysis of subtype-specific cellular composition with over 90% accuracy and identified drug-responsive leukemia subpopulations among a mixed suspension of cells modeling different AML subtypes. The adoption of machine learning algorithms for processing the large-scale nanoneedle-based miRNA data shows the potential for powerful prediction capability in clinical applications to assist therapeutic decisions. We believe that this platform provides an efficient and cost-effective solution to move forward the translational prognostic usage of miRNAs in AML treatment and can be readily and advantageously applied in analyzing rare patient-derived clinical samples.


Persistent Identifierhttp://hdl.handle.net/10722/338939
ISSN
2023 Impact Factor: 12.8
2023 SCImago Journal Rankings: 3.016
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhao, X-
dc.contributor.authorWang, ZX-
dc.contributor.authorJi, XL-
dc.contributor.authorBu, SY-
dc.contributor.authorFang, PL-
dc.contributor.authorWang, Y-
dc.contributor.authorWang, MX-
dc.contributor.authorYang, Y-
dc.contributor.authorZhang, WJ-
dc.contributor.authorLeung, AYH-
dc.contributor.authorShi, P-
dc.date.accessioned2024-03-11T10:32:41Z-
dc.date.available2024-03-11T10:32:41Z-
dc.date.issued2022-10-25-
dc.identifier.citationBiomaterials, 2022, v. 291-
dc.identifier.issn0142-9612-
dc.identifier.urihttp://hdl.handle.net/10722/338939-
dc.description.abstract<p>Acute myeloid leukemia (AML) is a highly heterogenous cancer in <a href="https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/hematopoiesis" title="Learn more about hematopoiesis from ScienceDirect's AI-generated Topic Pages">hematopoiesis</a>, and its subtype specification is greatly important in the clinical practice for AML diagnosis and prognosis. Increasing evidence has shown the association between <a href="https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/microrna" title="Learn more about microRNA from ScienceDirect's AI-generated Topic Pages">microRNA</a> (miRNA) phenotype and AML therapeutic outcomes, emphasizing the need for novel techniques for convenient, sensitive, and efficient miRNA profiling in clinical practices. Here, we describe a nanoneedle-based discrete single-cell <a href="https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/microrna" title="Learn more about microRNA from ScienceDirect's AI-generated Topic Pages">microRNA</a> profiling technique for multiplexed phenotyping of AML heterogeneity without the requirement of sequencing or <a href="https://www.sciencedirect.com/topics/materials-science/polymerase-chain-reaction" title="Learn more about polymerase chain reaction from ScienceDirect's AI-generated Topic Pages">polymerase chain reaction</a> (PCR). In virtue of a biochip-based and non-destructive nature of the assay, the expression of nine miRNAs in large number of living AML cells can be simultaneously analyzed with discrete single-cell level information, thus providing a proof-of-concept demonstration of an AML subtype classifier based on the multidimensional miRNA data. We showed successful analysis of subtype-specific cellular composition with over 90% accuracy and identified drug-responsive leukemia subpopulations among a mixed suspension of cells modeling different AML subtypes. The adoption of <a href="https://www.sciencedirect.com/topics/engineering/machine-learning-algorithm" title="Learn more about machine learning algorithms from ScienceDirect's AI-generated Topic Pages">machine learning algorithms</a> for processing the large-scale nanoneedle-based miRNA data shows the potential for powerful prediction capability in clinical applications to assist therapeutic decisions. We believe that this platform provides an efficient and cost-effective solution to move forward the translational prognostic usage of miRNAs in AML treatment and can be readily and advantageously applied in analyzing rare patient-derived clinical samples.</p>-
dc.languageeng-
dc.publisherElsevier-
dc.relation.ispartofBiomaterials-
dc.subjectAcute myeloid leukemia-
dc.subjectAML subtype-
dc.subjectCancer prognosis-
dc.subjectCellular heterogeneity-
dc.subjectmiRNA profiling-
dc.subjectNanomedicine-
dc.titleDiscrete single-cell microRNA analysis for phenotyping the heterogeneity of acute myeloid leukemia-
dc.typeArticle-
dc.identifier.doi10.1016/j.biomaterials.2022.121869-
dc.identifier.scopuseid_2-s2.0-85140985848-
dc.identifier.volume291-
dc.identifier.isiWOS:000880077600003-
dc.identifier.issnl0142-9612-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats