Author Correction: Improved genome recovery and integrated cell-size analyses of individual uncultured microbial cells and viral particles

Stepanauskas, Ramunas; Fergusson, Elizabeth; Brown, Joseph; Poulton, Nicole; Tupper, Ben; Labonté, Jessica M.; Becraft, Eric D.; Brown, Julia; Pachiadaki, Maria; Povilaitis, Tadas; Thompson, Brian; Mascena, Corianna J.; Bellows, Wendy K.; Lubys, Arvydas

doi:10.1038/s41467-017-02128-5

Cited by 6 publications

(7 citation statements)

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“…This challenge cannot be attributed to contamination of environmental DNA, since the MDA bias has persisted even after we introduced the “all-in-one” design for the RACE-Seq chip . Therefore, further optimization of the MDA parameters such as reducing total volume of reaction, ,, employing protein priming, normalizing postamplification, and utilizing a thermostable mutant of the phi29 polymerase will be warranted, although RACE operations likely pose a cap on the maximal sequencing coverage that can be accomplished.…”

Section: Resultsmentioning

confidence: 99%

Rational Optimization of Raman-Activated Cell Ejection and Sequencing for Bacteria

Gong

Gou

et al. 2020

Anal. Chem.

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In Raman-activated cell ejection and sequencing (RACE-Seq), success rate and sequence coverage have generally been low for shotgun sequencing of individual post-RACE cells. Here we quantitatively evaluated the influence of cell lysis condition, nucleic acid amplification condition, and parameters of Raman measurement on RACE-Seq performance. Variations in laser energy input during Raman signal acquisition, but not duration of alkaline lysate lysis, temperature, or measurement under dry or aqueous conditions, are vital to the success of multiple displacement amplification (MDA). In fact, laser irradiation is reversely linked to MDA product quality. However, introduction of oils prior to MDA, by mitigating such negative effects of Raman irradiation, elevates genome coverage of post-RACE Escherichia coli cells from <20% to ∼50%, while greatly improving the success rate of RACE-Seq for soil microbiota. Our findings provide a practical solution for enhancing RACE-Seq performance and pinpoint protection of cells from laser irradiation as a priority in method development.

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Section: Resultsmentioning

confidence: 99%

Rational Optimization of Raman-Activated Cell Ejection and Sequencing for Bacteria

Gong

Gou

et al. 2020

Anal. Chem.

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“…It is generally assumed that forward scatter mostly reflects cell size, and that side scatter reflects surface properties such as granularity [53]. Several previous studies have established that FCM can be successfully used to distinguish bacteria of different shapes and sizes [40][41][42][43], i.e. the average scattering of a population of cells reflects the average size of the cells in the population.…”

Section: Plos Onementioning

confidence: 99%

“…Estimating the distribution of GFP concentrations directly using flow cytometry requires to not only estimate the total GFP but also the volume of individual cells. Although forwardand side-scatter signals can be used to distinguish the average size of populations of cells of sufficiently different shapes and sizes [40][41][42][43], it is substantially more challenging to accurately quantify the relatively small cell-to-cell variations in cell volume for populations of isogenic bacteria growing in a homogeneous environment. In line with previous works [35,[44][45][46] we find that, because forward-and side-scattering measurements depend on cell volume in a complex non-linear manner and contain a substantial amount of shot noise that cannot be easily calibrated, it is impossible to accurately quantify the sizes of individual cells.…”

Section: Introductionmentioning

confidence: 99%

Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria

et al. 2020

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Fluorescence flow cytometry is increasingly being used to quantify single-cell expression distributions in bacteria in high-throughput. However, there has been no systematic investigation into the best practices for quantitative analysis of such data, what systematic biases exist, and what accuracy and sensitivity can be obtained. We investigate these issues by measuring the same E. coli strains carrying fluorescent reporters using both flow cytometry and microscopic setups and systematically comparing the resulting single-cell expression distributions. Using these results, we develop methods for rigorous quantitative inference of single-cell expression distributions from fluorescence flow cytometry data. First, we present a Bayesian mixture model to separate debris from viable cells using all scattering signals. Second, we show that cytometry measurements of fluorescence are substantially affected by autofluorescence and shot noise, which can be mistaken for intrinsic noise in gene expression, and present methods to correct for these using calibration measurements. Finally, we show that because forward-and side-scatter signals scale non-linearly with cell size, and are also affected by a substantial shot noise component that cannot be easily calibrated unless independent measurements of cell size are available, it is not possible to accurately estimate the variability in the sizes of individual cells using flow cytometry measurements alone. To aid other researchers with quantitative analysis of flow cytometry expression data in bacteria, we distribute E-Flow, an open-source R package that implements our methods for filtering debris and for estimating true biological expression means and variances from the fluorescence signal. The package is

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“…To test the specificity of the sorting and to conduct single-cell genomics, a previously modified and improved multiple displacement amplification (MDA) method called WGA-X ( 53 ) was applied to the sorted microcapsules. At first, MDA was unsuccessful (0%, n = 76) with both positive and negative capsules, i.e., regardless of the GC content of the bacterial genome or the presence of silver granules.…”

Section: Resultsmentioning

confidence: 99%

scMAR-Seq: a novel workflow for targeted single-cell genomics of microorganisms using radioactive labeling

Lo,

Wink,

Nitz

et al. 2023

mSystems

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Current methods for the identification of specific microorganisms based on an in situ metabolism are often hampered by insufficient sensitivity and habitat complexity. Here, we present a novel approach for identifying and sequencing single microbial cells metabolizing a specific organic compound with high sensitivity and without prior knowledge of the microbial community. The workflow consists of labeling individual cells with a [ 14 C] substrate based on their metabolic activity, followed by encapsulating cells in alginate with nuclear emulsion by using microfluidics. We here adapted the concept of microautoradiography to visually distinguish between encapsulated labeled and non-labeled cells, which are then sorted via flow cytometry for single cell genomics. As a proof-of-concept, we labeled, separated, lysed, and sequenced single cells of the benzene degrader Pseudomonas veronii from mock microbial communities. The cells of P. veronii were isolated with 100% specificity. Single-cell microautoradiography and genome sequencing is an innovative method for elucidating microbial identity, activity, and function in diverse habitats, contributing to elucidate novel taxa and genes with potential for biotechnological applications such as bioremediation. IMPORTANCE A central question in microbial ecology is which member of a community performs a particular metabolism. Several sophisticated isotope labeling techniques are available for analyzing the metabolic function of populations and individual cells in a community. However, these methods are generally either insufficiently sensitive or throughput-limited and thus have limited applicability for the study of complex environmental samples. Here, we present a novel approach that combines highly sensitive radioisotope tracking, microfluidics, high-throughput sorting, and single-cell genomics to simultaneously detect and identify individual microbial cells based solely on their in situ metabolic activity, without prior information on community structure.

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Author Correction: Improved genome recovery and integrated cell-size analyses of individual uncultured microbial cells and viral particles

Cited by 6 publications

References 0 publications

Rational Optimization of Raman-Activated Cell Ejection and Sequencing for Bacteria

Rational Optimization of Raman-Activated Cell Ejection and Sequencing for Bacteria

Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria

scMAR-Seq: a novel workflow for targeted single-cell genomics of microorganisms using radioactive labeling

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