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iLABMED 2023, 1(1): 5-14 https://doi.org/10.1002/ila2.12
Commentary | Open Access | Issue | Published: 09 May 2023

Single cell metabolic phenome and genome via the ramanome technology platform: Precision medicine of infectious diseases at the ultimate precision?

Show Author's Information Jian Xu1 ( ),  Jianzhong Zhang2,  Yingchun Xu3,  Yi‐Wei Tang4,  Bo Ma1,5,  Yuzhang Wu6,( )
Single‐Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong, China
State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
Department of Clinical Laboratory, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
Medical Affairs, Danaher Corporation/Cepheid, Shanghai, China
Qingdao Single‐Cell Biotech. Co., Ltd., Qingdao, Shandong, China
Chongqing International Institute of Immunology, Chongqing, Sichuan, China

[Corrections added on 24 May 2023, after first online publication: 1. Expansion of CAST‐R has been corrected in the Abbreviations section. 2. Microbial dingle‐cell has been corrected as Microbial single‐cell in the figure 1 caption.]

Keywords:
diagnosis of microbial infections, phenome, ramanome, Raman‐activated cell sorting (RACS), single cell sequencing
Cite this article:
Xu J, Zhang J, Xu Y, et al. Single cell metabolic phenome and genome via the ramanome technology platform: Precision medicine of infectious diseases at the ultimate precision?. iLABMED, 2023, 1(1): 5-14. https://doi.org/10.1002/ila2.12
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Abstract Full text About this article

Due to the limitations of existing approaches, a rapid, sensitive, accurate, comprehensive, and generally applicable strategy to diagnose and treat bacterial and fungal infections remains a major challenge. Here, based on the ramanome technology platform, we propose a culture‐free, one cell resolution, phenome‐genome‐combined strategy called single‐cell identification, viability and vitality tests and source tracking (SCIVVS). For each cell directly extracted from a clinical specimen, the fingerprint region of the D2O‐probed single cell Raman spectrum (SCRS) enables species‐level identification based on a reference SCRS database of pathogen species, whereas the C‐D band accurately quantifies viability, metabolic vitality, phenotypic susceptibility to antimicrobials, and their intercellular heterogeneity. Moreover, to source track a cell, Raman‐activated cell sorting followed by sequencing or cultivation proceeds, producinging an indexed, high coverage genome assembly or a pure culture from precisely one pathogenic cell. Finally, an integrated SCIVVS workflow that features automated profiling and sorting of metabolic and morphological phenomes can complete the entire process in only a few hours. Because it resolves heterogeneity for both the metabolic phenome and genome, targets functions, can be automated, and is orders‐of‐magnitude faster while cost‐effective, SCIVVS is a new technological and data framework to diagnose and treat bacterial and fungal infections in various clinical and disease control settings.


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About this article

Single cell metabolic phenome and genome via the ramanome technology platform: Precision medicine of infectious diseases at the ultimate precision?

Show Author's information Jian Xu1 ( ), Jianzhong Zhang2, Yingchun Xu3, Yi‐Wei Tang4, Bo Ma1,5, Yuzhang Wu6,( )
Single‐Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong, China
State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
Department of Clinical Laboratory, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
Medical Affairs, Danaher Corporation/Cepheid, Shanghai, China
Qingdao Single‐Cell Biotech. Co., Ltd., Qingdao, Shandong, China
Chongqing International Institute of Immunology, Chongqing, Sichuan, China

[Corrections added on 24 May 2023, after first online publication: 1. Expansion of CAST‐R has been corrected in the Abbreviations section. 2. Microbial dingle‐cell has been corrected as Microbial single‐cell in the figure 1 caption.]

Abstract

Due to the limitations of existing approaches, a rapid, sensitive, accurate, comprehensive, and generally applicable strategy to diagnose and treat bacterial and fungal infections remains a major challenge. Here, based on the ramanome technology platform, we propose a culture‐free, one cell resolution, phenome‐genome‐combined strategy called single‐cell identification, viability and vitality tests and source tracking (SCIVVS). For each cell directly extracted from a clinical specimen, the fingerprint region of the D2O‐probed single cell Raman spectrum (SCRS) enables species‐level identification based on a reference SCRS database of pathogen species, whereas the C‐D band accurately quantifies viability, metabolic vitality, phenotypic susceptibility to antimicrobials, and their intercellular heterogeneity. Moreover, to source track a cell, Raman‐activated cell sorting followed by sequencing or cultivation proceeds, producinging an indexed, high coverage genome assembly or a pure culture from precisely one pathogenic cell. Finally, an integrated SCIVVS workflow that features automated profiling and sorting of metabolic and morphological phenomes can complete the entire process in only a few hours. Because it resolves heterogeneity for both the metabolic phenome and genome, targets functions, can be automated, and is orders‐of‐magnitude faster while cost‐effective, SCIVVS is a new technological and data framework to diagnose and treat bacterial and fungal infections in various clinical and disease control settings.

Keywords: diagnosis of microbial infections, phenome, ramanome, Raman‐activated cell sorting (RACS), single cell sequencing

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Published: 09 May 2023
Issue date: June 2023

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© 2023 The Authors. Tsinghua University Press.

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ACKNOWLEDGMENTS

We thank Yang Liu for graphics support. This study was funded by the National Key R&D Program of China (2022YFA1304101), CAS (XDB29050400), the National Natural Science Foundation of China (32030003), and Shenzhen ‐ Hong Kong Innovation Circle Plan (SGDX2019081623060946).

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