scholarly journals Measuring phenotypic heterogeneity in isogenic bacterial populations using flow cytometry and Raman spectroscopy

2019 ◽  
Author(s):  
Cristina García-Timermans ◽  
Peter Rubbens ◽  
Jasmine Heyse ◽  
Frederiek-Maarten Kerckhof ◽  
Ruben Props ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Raman Spectroscopy ◽  
Data Analysis ◽  
Single Cell ◽  
Population Level ◽  
Phenotypic Heterogeneity ◽  
Single Cell Level ◽  
Analysis Framework ◽  
Cell Level ◽  
Bacterial Populations

AbstractInvestigating phenotypic heterogeneity can help to better understand and manage microbial communities. However, characterizing phenotypic heterogeneity remains a challenge, as there is no standardized analysis framework. Several optical tools are available, which often describe properties of the individual cell. In this work, we compare Raman spectroscopy and flow cytometry to study phenotypic heterogeneity in bacterial populations. The growth phase of E. coli populations was characterized using both technologies. Our findings show that flow cytometry detects and quantifies shifts in phenotypic heterogeneity at the population level due to its high-throughput nature. Raman spectroscopy, on the other hand, offers a much higher resolution at the single-cell level (i.e. more biochemical information is recorded). Therefore, it is capable of identifying distinct phenotypic populations when coupled with standardized data analysis. In addition, it provides information about biomolecules that are present, which can be linked to cell functionality. We propose an automated workflow to distinguish between bacterial phenotypic populations using Raman spectroscopy and validated this approach with an external dataset. We recommend to apply flow cytometry to characterize phenotypic heterogeneity at the population level, and Raman spectroscopy to perform a more in-depth analysis of heterogeneity at the single-cell level.ImportanceSingle-cell techniques are frequently applied tools to study phenotypic characteristics of bacterial populations. As flow cytometry and Raman spectroscopy gain popularity in the field, there is a need to understand their advantages and limitations, as well as to create a more standardized data analysis framework. Our work shows that flow cytometry allows to study and quantify shifts at the bacterial population level, but since its resolution is limited for microbial purposes, distinct phenotypic populations cannot be distinguished at the single-cell level. Raman spectroscopy, combined with appropriate data analysis, has sufficient resolving power at the single-cell level, enabling the identification of distinct phenotypic populations. As regions in a Raman spectrum are associated with specific (bio)molecules, it is possible to link these to the cell state and/or its function.

scholarly journals Discriminating Bacterial Phenotypes at the Population and Single‐Cell Level: A Comparison of Flow Cytometry and Raman Spectroscopy Fingerprinting

2019 ◽  
Vol 97 (7) ◽  
pp. 713-726 ◽  
Author(s):  
Cristina García‐Timermans ◽  
Peter Rubbens ◽  
Jasmine Heyse ◽  
Frederiek‐Maarten Kerckhof ◽  
Ruben Props ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Raman Spectroscopy ◽  
Single Cell ◽  
Single Cell Level ◽  
Cell Level

scholarly journals Coculturing Bacteria Leads to Reduced Phenotypic Heterogeneities

2019 ◽  
Vol 85 (8) ◽  
Author(s):  
Jasmine Heyse ◽  
Benjamin Buysschaert ◽  
Ruben Props ◽  
Peter Rubbens ◽  
Andre G. Skirtach ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Raman Spectroscopy ◽  
Single Cell ◽  
Phenotypic Diversity ◽  
Microbial Interactions ◽  
Phenotypic Heterogeneity ◽  
Bacterial Populations ◽  
The Individual ◽  
Axenic Conditions ◽  
The Impact

ABSTRACT Isogenic bacterial populations are known to exhibit phenotypic heterogeneity at the single-cell level. Because of difficulties in assessing the phenotypic heterogeneity of a single taxon in a mixed community, the importance of this deeper level of organization remains relatively unknown for natural communities. In this study, we have used membrane-based microcosms that allow the probing of the phenotypic heterogeneity of a single taxon while interacting with a synthetic or natural community. Individual taxa were studied under axenic conditions, as members of a coculture with physical separation, and as a mixed culture. Phenotypic heterogeneity was assessed through both flow cytometry and Raman spectroscopy. Using this setup, we investigated the effect of microbial interactions on the individual phenotypic heterogeneities of two interacting drinking water isolates. Through flow cytometry we have demonstrated that interactions between these bacteria lead to a reduction of their individual phenotypic diversities and that this adjustment is conditional on the bacterial taxon. Single-cell Raman spectroscopy confirmed a taxon-dependent phenotypic shift due to the interaction. In conclusion, our data suggest that bacterial interactions may be a general driver of phenotypic heterogeneity in mixed microbial populations. IMPORTANCE Laboratory studies have shown the impact of phenotypic heterogeneity on the survival and functionality of isogenic populations. Because phenotypic heterogeneity plays an important role in pathogenicity and virulence, antibiotic resistance, biotechnological applications, and ecosystem properties, it is crucial to understand its influencing factors. An unanswered question is whether bacteria in mixed communities influence the phenotypic heterogeneity of their community partners. We found that coculturing bacteria leads to a reduction in their individual phenotypic heterogeneities, which led us to the hypothesis that the individual phenotypic diversity of a taxon is dependent on the community composition.

scholarly journals Heteroresistance at the Single-Cell Level: Adapting to Antibiotic Stress through a Population-Based Strategy and Growth-Controlled Interphenotypic Coordination

mBio ◽  
2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Xiaorong Wang ◽  
Yu Kang ◽  
Chunxiong Luo ◽  
Tong Zhao ◽  
Lin Liu ◽  
...  
Keyword(s):  
Growth Rate ◽  
Single Cell ◽  
Task Allocation ◽  
Bacterial Population ◽  
Population Based ◽  
Phenotypic Heterogeneity ◽  
Single Cell Level ◽  
Bacterial Survival ◽  
Cell Level ◽  
Resistant Cells

ABSTRACT Heteroresistance refers to phenotypic heterogeneity of microbial clonal populations under antibiotic stress, and it has been thought to be an allocation of a subset of “resistant” cells for surviving in higher concentrations of antibiotic. The assumption fits the so-called bet-hedging strategy, where a bacterial population “hedges” its “bet” on different phenotypes to be selected by unpredicted environment stresses. To test this hypothesis, we constructed a heteroresistance model by introducing a bla CTX-M-14 gene (coding for a cephalosporin hydrolase) into a sensitive Escherichia coli strain. We confirmed heteroresistance in this clone and that a subset of the cells expressed more hydrolase and formed more colonies in the presence of ceftriaxone (exhibited stronger “resistance”). However, subsequent single-cell-level investigation by using a microfluidic device showed that a subset of cells with a distinguishable phenotype of slowed growth and intensified hydrolase expression emerged, and they were not positively selected but increased their proportion in the population with ascending antibiotic concentrations. Therefore, heteroresistance—the gradually decreased colony-forming capability in the presence of antibiotic—was a result of a decreased growth rate rather than of selection for resistant cells. Using a mock strain without the resistance gene, we further demonstrated the existence of two nested growth-centric feedback loops that control the expression of the hydrolase and maximize population growth in various antibiotic concentrations. In conclusion, phenotypic heterogeneity is a population-based strategy beneficial for bacterial survival and propagation through task allocation and interphenotypic collaboration, and the growth rate provides a critical control for the expression of stress-related genes and an essential mechanism in responding to environmental stresses. IMPORTANCE Heteroresistance is essentially phenotypic heterogeneity, where a population-based strategy is thought to be at work, being assumed to be variable cell-to-cell resistance to be selected under antibiotic stress. Exact mechanisms of heteroresistance and its roles in adaptation to antibiotic stress have yet to be fully understood at the molecular and single-cell levels. In our study, we have not been able to detect any apparent subset of “resistant” cells selected by antibiotics; on the contrary, cell populations differentiate into phenotypic subsets with variable growth statuses and hydrolase expression. The growth rate appears to be sensitive to stress intensity and plays a key role in controlling hydrolase expression at both the bulk population and single-cell levels. We have shown here, for the first time, that phenotypic heterogeneity can be beneficial to a growing bacterial population through task allocation and interphenotypic collaboration other than partitioning cells into different categories of selective advantage.

Monitoring human leukocyte antigen class I molecules by micro-Raman spectroscopy at single-cell level

2010 ◽  
Vol 15 (2) ◽  
pp. 027007 ◽  
Author(s):  
Gobind Das ◽  
Rosanna La Rocca ◽  
Tadepally Lakshmikanth ◽  
Francesco Gentile ◽  
Rossana Tallerico ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Human Leukocyte Antigen ◽  
Single Cell ◽  
Human Leukocyte Antigen Class ◽  
Human Leukocyte ◽  
Class I ◽  
Single Cell Level ◽  
Cell Level ◽  
Leukocyte Antigen ◽  
Micro Raman Spectroscopy

scholarly journals Effect of Laser Irradiation on Cell Function and Its Implications in Raman Spectroscopy

2018 ◽  
Vol 84 (8) ◽  
pp. e02508-17 ◽  
Author(s):  
Xiaofei Yuan ◽  
Yanqing Song ◽  
Yizhi Song ◽  
Jiabao Xu ◽  
Yinhu Wu ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Cell Growth ◽  
Single Cell ◽  
Laser Irradiation ◽  
Living Cells ◽  
Growth Characteristics ◽  
Single Cell Level ◽  
Bacterial Cells ◽  
Cell Level ◽  
Gram Negative

ABSTRACTLasers are instrumental in advanced bioimaging and Raman spectroscopy. However, they are also well known for their destructive effects on living organisms, leading to concerns about the adverse effects of laser technologies. To implement Raman spectroscopy for cell analysis and manipulation, such as Raman-activated cell sorting, it is crucial to identify nondestructive conditions for living cells. Here, we evaluated quantitatively the effect of 532-nm laser irradiation on bacterial cell fate and growth at the single-cell level. Using a purpose-built microfluidic platform, we were able to quantify the growth characteristics, i.e., specific growth rates and lag times of individual cells, as well as the survival rate of a population in conjunction with Raman spectroscopy. Representative Gram-negative and Gram-positive species show similar trends in response to a laser irradiation dose. Laser irradiation could compromise the physiological function of cells, and the degree of destruction is both dose and strain dependent, ranging from reduced cell growth to a complete loss of cell metabolic activity and finally to physical disintegration. Gram-positive bacterial cells are more susceptible than Gram-negative bacterial strains to irradiation-induced damage. By directly correlating Raman acquisition with single-cell growth characteristics, we provide evidence of nondestructive characteristics of Raman spectroscopy on individual bacterial cells. However, while strong Raman signals can be obtained without causing cell death, the variety of responses from different strains and from individual cells justifies careful evaluation of Raman acquisition conditions if cell viability is critical.IMPORTANCEIn Raman spectroscopy, the use of powerful monochromatic light in laser-based systems facilitates the detection of inherently weak signals. This allows environmentally and clinically relevant microorganisms to be measured at the single-cell level. The significance of being able to perform Raman measurement is that, unlike label-based fluorescence techniques, it provides a “fingerprint” that is specific to the identity and state of any (unlabeled) sample. Thus, it has emerged as a powerful method for studying living cells under physiological and environmental conditions. However, the laser's high power also has the potential to kill bacteria, which leads to concerns. The research presented here is a quantitative evaluation that provides a generic platform and methodology to evaluate the effects of laser irradiation on individual bacterial cells. Furthermore, it illustrates this by determining the conditions required to nondestructively measure the spectra of representative bacteria from several different groups.

scholarly journals High-throughput detection of miRNAs and gene-specific mRNA at the single-cell level by flow cytometry

2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Filippos Porichis ◽  
Meghan G. Hart ◽  
Morgane Griesbeck ◽  
Holly L. Everett ◽  
Muska Hassan ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Single Cell Level ◽  
Cell Level ◽  
High Throughput Detection ◽  
Specific Mrna

Alterations of Raman profiles of nasopharyngeal carcinoma cells after treated with chemodrugs detected by laser tweezer Raman spectroscopy.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15513-e15513
Author(s):  
Miaomiao Li ◽  
Xiaochuan Chen ◽  
Ting Lin ◽  
Zongwei Huang ◽  
Shihong Wu ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Nasopharyngeal Carcinoma ◽  
Single Cell ◽  
Control Group ◽  
Single Cell Level ◽  
Label Free ◽  
Cell Level ◽  
Analytical Strategy ◽  
Linear Discriminant ◽  
Laser Tweezer

e15513 Background: To explore the metabolic alterations of nasopharyngeal carcinoma (NPC) cells after treated with chemodrugs, the Raman profiles were characterized with laser tweezer Raman spectroscopy. Methods: Two NPC cell lines (CNE2 and C666-1) were treated with gemcitabine, cisplatin, and paclitaxel, respectively. The high-quality Raman spectra of cells without or with treatments were recorded at the single-cell level with label-free laser tweezers Raman spectroscopy (LTRS) and analyzed for the differences of alterations of Raman profiles. Results: Tentative assignments of Raman peaks indicated that the cellular specific biomolecular changes associated with drug treatment, including changes in protein structure (e.g. 1655 cm−1), changes in DNA content and structure (e.g. 830 cm−1), destruction of DNA base pairs (e.g. 785 cm−1), and reduction in lipids (e.g. 970 cm−1). Besides, both principal components analysis (PCA) combined with linear discriminant analysis (LDA) and the classification and regression trees (CRT) algorithms were employed to further analyze and classify the spectral data between control group and treated group, with the best discriminant accuracy of 96.7% and 90.0% for CNE2 and C666-1 group treated with paclitaxel, respectively. Conclusions: This exploratory work demonstrated that LTRS technology combined with multivariate statistical analysis has promising potential to be a novel analytical strategy at the single-cell level for the evaluation of NPC-related chemotherapeutic drugs.

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