Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Population of Cells

ABSTRACT To reveal the dynamic features of cellular systems, such as the correlation among phenotypes, a time or condition series set of samples is typically required. Here, we propose intra-ramanome correlation analysis (IRCA) to achieve this goal from just one snapshot of an isogenic population, v...

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Autores principales:	Yuehui He, Shi Huang, Peng Zhang, Yuetong Ji, Jian Xu
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2021
Materias:	ramanome intra-ramanome correlation analysis (IRCA) intra-ramanome correlation network (IRCN) single-cell Raman spectroscopy phenotypic heterogeneity Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/c31980330dfd4439aaaef15adda75a24
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spelling	oai:doaj.org-article:c31980330dfd4439aaaef15adda75a242021-11-10T18:37:51ZIntra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Population of Cells10.1128/mBio.01470-212150-7511https://doaj.org/article/c31980330dfd4439aaaef15adda75a242021-08-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mBio.01470-21https://doaj.org/toc/2150-7511ABSTRACT To reveal the dynamic features of cellular systems, such as the correlation among phenotypes, a time or condition series set of samples is typically required. Here, we propose intra-ramanome correlation analysis (IRCA) to achieve this goal from just one snapshot of an isogenic population, via pairwise correlation among the cells of the thousands of Raman peaks in single-cell Raman spectra (SCRS), i.e., by taking advantage of the intrinsic metabolic heterogeneity among individual cells. For example, IRCA of Chlamydomonas reinhardtii under nitrogen depletion revealed metabolite conversions at each time point plus their temporal dynamics, such as protein-to-starch conversion followed by starch-to-triacylglycerol (TAG) conversion, and conversion of membrane lipids to TAG. Such among-cell correlations in SCRS vanished when the starch-biosynthesis pathway was knocked out yet were fully restored by genetic complementation. Extension of IRCA to 64 microalgal, fungal, and bacterial ramanomes suggests the IRCA-derived metabolite conversion network as an intrinsic metabolic signature of isogenic cellular population that is reliable, species-resolved, and state-sensitive. The high-throughput, low cost, excellent scalability, and general extendibility of IRCA suggest its broad applications. IMPORTANCE Each isogenic population of cells is characterized by many phenotypes, which change with time and condition. Correlations among such phenotypes are fundamental to system function, yet revelation of such links typically requires multiple samples. Here, we showed that, by exploiting the intrinsic metabolic heterogeneity among individual cells, such interphenotype correlations can be unveiled via just one snapshot of an isogenic cellular population. Specifically, a network of potential metabolite conversions can be reconstructed using intra-ramanome correlation analysis (IRCA), by pairwise correlation of the thousands of Raman peaks or combination of peaks among single-cell Raman spectra sampled from just one instance of the cellular population. The ability to rapidly and noninvasively reveal intermetabolite conversions from just one snapshot of one sample should usher in many new opportunities in functional profiling of cellular systems.Yuehui HeShi HuangPeng ZhangYuetong JiJian XuAmerican Society for Microbiologyarticleramanomeintra-ramanome correlation analysis (IRCA)intra-ramanome correlation network (IRCN)single-cell Raman spectroscopyphenotypic heterogeneityMicrobiologyQR1-502ENmBio, Vol 12, Iss 4 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ramanome intra-ramanome correlation analysis (IRCA) intra-ramanome correlation network (IRCN) single-cell Raman spectroscopy phenotypic heterogeneity Microbiology QR1-502
spellingShingle	ramanome intra-ramanome correlation analysis (IRCA) intra-ramanome correlation network (IRCN) single-cell Raman spectroscopy phenotypic heterogeneity Microbiology QR1-502 Yuehui He Shi Huang Peng Zhang Yuetong Ji Jian Xu Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Population of Cells
description	ABSTRACT To reveal the dynamic features of cellular systems, such as the correlation among phenotypes, a time or condition series set of samples is typically required. Here, we propose intra-ramanome correlation analysis (IRCA) to achieve this goal from just one snapshot of an isogenic population, via pairwise correlation among the cells of the thousands of Raman peaks in single-cell Raman spectra (SCRS), i.e., by taking advantage of the intrinsic metabolic heterogeneity among individual cells. For example, IRCA of Chlamydomonas reinhardtii under nitrogen depletion revealed metabolite conversions at each time point plus their temporal dynamics, such as protein-to-starch conversion followed by starch-to-triacylglycerol (TAG) conversion, and conversion of membrane lipids to TAG. Such among-cell correlations in SCRS vanished when the starch-biosynthesis pathway was knocked out yet were fully restored by genetic complementation. Extension of IRCA to 64 microalgal, fungal, and bacterial ramanomes suggests the IRCA-derived metabolite conversion network as an intrinsic metabolic signature of isogenic cellular population that is reliable, species-resolved, and state-sensitive. The high-throughput, low cost, excellent scalability, and general extendibility of IRCA suggest its broad applications. IMPORTANCE Each isogenic population of cells is characterized by many phenotypes, which change with time and condition. Correlations among such phenotypes are fundamental to system function, yet revelation of such links typically requires multiple samples. Here, we showed that, by exploiting the intrinsic metabolic heterogeneity among individual cells, such interphenotype correlations can be unveiled via just one snapshot of an isogenic cellular population. Specifically, a network of potential metabolite conversions can be reconstructed using intra-ramanome correlation analysis (IRCA), by pairwise correlation of the thousands of Raman peaks or combination of peaks among single-cell Raman spectra sampled from just one instance of the cellular population. The ability to rapidly and noninvasively reveal intermetabolite conversions from just one snapshot of one sample should usher in many new opportunities in functional profiling of cellular systems.
format	article
author	Yuehui He Shi Huang Peng Zhang Yuetong Ji Jian Xu
author_facet	Yuehui He Shi Huang Peng Zhang Yuetong Ji Jian Xu
author_sort	Yuehui He
title	Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Population of Cells
title_short	Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Population of Cells
title_full	Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Population of Cells
title_fullStr	Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Population of Cells
title_full_unstemmed	Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Population of Cells
title_sort	intra-ramanome correlation analysis unveils metabolite conversion network from an isogenic population of cells
publisher	American Society for Microbiology
publishDate	2021
url	https://doaj.org/article/c31980330dfd4439aaaef15adda75a24
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Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Population of Cells

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