Metabolic responses of indigenous bacteria in chicken faeces and maggots to multiple antibiotics via heavy water labeled single-cell Raman spectroscopy

2022 ◽  
Vol 113 ◽  
pp. 394-402
Author(s):  
Oladipo Oladiti Olaniyi ◽  
Hongzhe Li ◽  
Yongguan Zhu ◽  
Li Cui
Keyword(s):  
Raman Spectroscopy ◽  
Single Cell ◽  
Heavy Water ◽  
Metabolic Responses ◽  
Indigenous Bacteria ◽  
Multiple Antibiotics

scholarly journals Single-cell discrimination based on optical tweezers Raman spectroscopy

2013 ◽  
Vol 58 (21) ◽  
pp. 2594-2600 ◽  
Author(s):  
HongFei Ma ◽  
Yong Zhang ◽  
AnPei Ye
Keyword(s):  
Raman Spectroscopy ◽  
Single Cell ◽  
Optical Tweezers ◽  
Cell Discrimination

Dynamic characterization of drug resistance and heterogeneity of the gastric cancer cell BGC823 using single-cell Raman spectroscopy

The Analyst ◽  
2018 ◽  
Vol 143 (1) ◽  
pp. 164-174 ◽  
Author(s):  
Yong Zhang ◽  
Ludi Jin ◽  
Jingjing Xu ◽  
Yuezhou Yu ◽  
Lin Shen ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Raman Spectroscopy ◽  
Drug Resistance ◽  
Gastric Carcinoma ◽  
Single Cell ◽  
Gastric Cancer Cell ◽  
Carcinoma Cells ◽  
Dynamic Characterization ◽  
Human Gastric Carcinoma Cells

Drug resistance and heterogeneous characteristics of human gastric carcinoma cells (BGC823) under the treatment of paclitaxel (PTX) were investigated using single-cell Raman spectroscopy (RS).

scholarly journals Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential

2020 ◽  
Author(s):  
Santosh Kumar Paidi ◽  
Vaani Shah ◽  
Piyush Raj ◽  
Kristine Glunde ◽  
Rishikesh Pandey ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Raman Spectroscopy ◽  
Molecular Imaging ◽  
Single Cell ◽  
Cancer Cells ◽  
Breast Cancer Cells ◽  
Metastatic Potential ◽  
Optical Diffraction ◽  
Multivariate Curve Resolution ◽  
Label Free

AbstractIdentification of the metastatic potential represents one of the most important tasks for molecular imaging of cancer. While molecular imaging of metastases has witnessed substantial progress as an area of clinical inquiry, determining precisely what differentiates the metastatic phenotype has proven to be more elusive underscoring the need to marry emerging imaging techniques with tumor biology. In this study, we utilize both the morphological and molecular information provided by 3D optical diffraction tomography and Raman spectroscopy, respectively, to propose a label-free route for optical phenotyping of cancer cells at single-cell resolution. By using an isogenic panel of cell lines derived from MDA-MB-231 breast cancer cells that vary in their metastatic potential, we show that 3D refractive index tomograms can capture subtle morphological differences among the parental, circulating tumor cells, and lung metastatic cells. By leveraging the molecular specificity of Raman spectroscopy, we demonstrate that coarse Raman microscopy is capable of rapidly mapping a sufficient number of cells for training a random forest classifier that can accurately predict the metastatic potential of cells at a single-cell level. We also leverage multivariate curve resolution – alternating least squares decomposition of the spectral dataset to demarcate spectra from cytoplasm and nucleus, and test the feasibility of identifying metastatic phenotypes using the spectra only from the cytoplasmic and nuclear regions. Overall, our study provides a rationale for employing coarse Raman mapping to substantially reduce measurement time thereby enabling the acquisition of reasonably large training datasets that hold the key for label-free single-cell analysis and, consequently, for differentiation of indolent from aggressive phenotypes.

scholarly journals Raman Spectroscopy-Based Measurements of Single-Cell Phenotypic Diversity in Microbial Populations

mSphere ◽  
2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Cristina García-Timermans ◽  
Ruben Props ◽  
Boris Zacchetti ◽  
Myrsini Sakarika ◽  
Frank Delvigne ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Single Cell ◽  
Raman Spectra ◽  
Phenotypic Diversity ◽  
Cell Permeability ◽  
Microbial Populations ◽  
Molecular Profile ◽  
Microbial Cells ◽  
Content Type ◽  
Stressed Cells

ABSTRACT Microbial cells experience physiological changes due to environmental change, such as pH and temperature, the release of bactericidal agents, or nutrient limitation. This has been shown to affect community assembly and physiological processes (e.g., stress tolerance, virulence, or cellular metabolic activity). Metabolic stress is typically quantified by measuring community phenotypic properties such as biomass growth, reactive oxygen species, or cell permeability. However, bulk community measurements do not take into account single-cell phenotypic diversity, which is important for a better understanding and the subsequent management of microbial populations. Raman spectroscopy is a nondestructive alternative that provides detailed information on the biochemical makeup of each individual cell. Here, we introduce a method for describing single-cell phenotypic diversity using the Hill diversity framework of Raman spectra. Using the biomolecular profile of individual cells, we obtained a metric to compare cellular states and used it to study stress-induced changes. First, in two Escherichia coli populations either treated with ethanol or nontreated and then in two Saccharomyces cerevisiae subpopulations with either high or low expression of a stress reporter. In both cases, we were able to quantify single-cell phenotypic diversity and to discriminate metabolically stressed cells using a clustering algorithm. We also described how the lipid, protein, and nucleic acid compositions changed after the exposure to the stressor using information from the Raman spectra. Our results show that Raman spectroscopy delivers the necessary resolution to quantify phenotypic diversity within individual cells and that this information can be used to study stress-driven metabolic diversity in microbial populations. IMPORTANCE Microbial cells that live in the same community can exist in different physiological and morphological states that change as a function of spatiotemporal variations in environmental conditions. This phenomenon is commonly known as phenotypic heterogeneity and/or diversity. Measuring this plethora of cellular expressions is needed to better understand and manage microbial processes. However, most tools to study phenotypic diversity only average the behavior of the sampled community. In this work, we present a way to quantify the phenotypic diversity of microbial samples by inferring the (bio)molecular profile of its constituent cells using Raman spectroscopy. We demonstrate how this tool can be used to quantify the phenotypic diversity that arises after the exposure of microbes to stress. Raman spectroscopy holds potential for the detection of stressed cells in bioproduction.

D2O-Isotope-Labeling Approach to Probing Phosphate-Solubilizing Bacteria in Complex Soil Communities by Single-Cell Raman Spectroscopy

2019 ◽  
Vol 91 (3) ◽  
pp. 2239-2246 ◽  
Author(s):  
Hong-Zhe Li ◽  
Qing-fang Bi ◽  
Kai Yang ◽  
Bang-Xiao Zheng ◽  
Qiang Pu ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Single Cell ◽  
Isotope Labeling ◽  
Phosphate Solubilizing Bacteria ◽  
Soil Communities ◽  
Labeling Approach ◽  
Phosphate Solubilizing
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