Detection and Quantification of Bacterial Autofluorescence at the Single-Cell Level by a Laboratory-Built High-Sensitivity Flow Cytometer

AbstractIron-reducing microorganisms (FeRM) play key roles in many natural and engineering processes. Visualizing and isolating FeRM from multispecies samples are essential to understand the in-situ location and geochemical role of FeRM. Here, we visualized FeRM by a “turn-on” Fe2+-specific fluorescent chemodosimeter (FSFC) with high sensitivity, selectivity and stability. This FSFC could selectively identify and locate active FeRM from either pure culture, co-culture of different bacteria or sediment-containing samples. Fluorescent intensity of the FSFC could be used as an indicator of Fe2+ concentration in bacterial cultures. By integrating FSFC with a single cell sorter, we obtained three FSFC-labeled cells from an enriched consortia and all of them were subsequently evidenced to be capable of iron-reduction and two unlabeled cells were evidenced to have no iron-reducing capability, further confirming the feasibility of the FSFC.ImportanceVisualization and isolation of FeRM from samples containing multispecies are commonly needed by researchers from different disciplines, such as environmental microbiology, environmental sciences and geochemistry. However, no available method has been reported. In this study, we provid a solution to visualize FeRM and evaluate their activity even at single cell level. Integrating with single cell sorter, FeRM can also be isolated from samples containing multispecies. This method can be used as a powerful tool to uncover the in-situ or ex-situ role of FeRM and their interactions with ambient microbes or chemicals.

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High Sensitivity In Vivo Imaging of Cancer Metastasis Using a Near-Infrared Luciferin Analogue seMpai

International Journal of Molecular Sciences ◽

10.3390/ijms21217896 ◽

2020 ◽

Vol 21 (21) ◽

pp. 7896

Author(s):

Jun Nakayama ◽

Ryohei Saito ◽

Yusuke Hayashi ◽

Nobuo Kitada ◽

Shota Tamaki ◽

...

Keyword(s):

Single Cell ◽

In Vivo Imaging ◽

Cancer Metastasis ◽

Near Infrared ◽

High Sensitivity ◽

Single Cell Level ◽

Background Signal ◽

Cell Level ◽

Highly Sensitive

Bioluminescence imaging (BLI) is useful to monitor cell movement and gene expression in live animals. However, D-luciferin has a short wavelength (560 nm) which is absorbed by tissues and the use of near-infrared (NIR) luciferin analogues enable high sensitivity in vivo BLI. The AkaLumine-AkaLuc BLI system (Aka-BLI) can detect resolution at the single-cell level; however, it has a clear hepatic background signal. Here, to enable the highly sensitive detection of bioluminescence from the surrounding liver tissues, we focused on seMpai (C15H16N3O2S) which has been synthesized as a luciferin analogue and has high luminescent abilities as same as AkaLumine. We demonstrated that seMpai BLI could detect micro-signals near the liver without any background signal. The solution of seMpai was neutral; therefore, seMpai imaging did not cause any adverse effect in mice. seMpai enabled a highly sensitive in vivo BLI as compared to previous techniques. Our findings suggest that the development of a novel mutated luciferase against seMpai may enable a highly sensitive BLI at the single-cell level without any background signal. Novel seMpai BLI system can be used for in vivo imaging in the fields of life sciences and medicine.

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Identifying tumor cells at the single cell level

10.1101/2021.10.15.463909 ◽

2021 ◽

Author(s):

Jan Dohmen ◽

Artem Baranovskii ◽

Bora Uyar ◽

Jonathan Ronen ◽

Vedran Franke ◽

...

Keyword(s):

Single Cell ◽

Tumor Cells ◽

Biomarker Discovery ◽

Single Cell Analysis ◽

High Sensitivity ◽

Simple Problem ◽

Cancer Therapies ◽

Single Cell Level ◽

Cell Level

Tumors are highly complex tissues composed of cancerous cells, surrounded by a heterogeneous cellular microenvironment. Tumor response to treatments is governed by an interaction of cancer cell intrinsic factors with external influences of the tumor microenvironment. Disentangling the heterogeneity within a tumor is a crucial step in developing and utilization of effective cancer therapies. Single cell sequencing has the potential to revolutionize personalized medicine. In cancer therapy it enables an effective characterization of the complete heterogeneity within the tumor. A governing challenge in cancer single cell analysis is cell annotation, the assignment of a particular cell type or a cell state to each sequenced cell. We propose Ikarus, a machine learning pipeline aimed at solving a perceived simple problem, distinguishing tumor cells from normal cells at the single cell level. Automatic characterization of tumor cells is a critical limiting step for a multitude of research, clinical, and commercial applications. Automatic characterization of tumor cells would expedite neoantigen prediction, automatic characterization of tumor cell states, it would greatly facilitate cancer biomarker discovery. Such a tool can be used for automatic annotation of histopathological data, profiled using multichannel immunofluorescence or spatial sequencing. We have tested ikarus on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts.

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Visualizing and isolating iron-reducing microorganisms at single cell level

Applied and Environmental Microbiology ◽

10.1128/aem.02192-20 ◽

2020 ◽

Author(s):

Cuifen Gan ◽

Rongrong Wu ◽

Yeshen Luo ◽

Jianhua Song ◽

Dizhou Luo ◽

...

Keyword(s):

Single Cell ◽

Iron Reduction ◽

High Sensitivity ◽

Ex Situ ◽

Single Cell Level ◽

Cell Level ◽

Cell Sorter ◽

Fluorescent Intensity

Iron-reducing microorganisms (FeRM) play key roles in many natural and engineering processes. Visualizing and isolating FeRM from multispecies samples are essential to understand the in-situ location and geochemical role of FeRM. Here, we visualized FeRM by a “turn-on” Fe2+-specific fluorescent chemodosimeter (FSFC) with high sensitivity, selectivity and stability. This FSFC could selectively identify and locate active FeRM from either pure culture, co-culture of different bacteria or sediment-containing samples. Fluorescent intensity of the FSFC could be used as an indicator of Fe2+ concentration in bacterial cultures. By integrating FSFC with a single cell sorter, we obtained three FSFC-labeled cells from an enriched consortia and all of them were subsequently evidenced to be capable of iron-reduction and two unlabeled cells were evidenced to have no iron-reducing capability, further confirming the feasibility of the FSFC. IMPORTANCE Visualization and isolation of FeRM from samples containing multispecies are commonly needed by researchers from different disciplines, such as environmental microbiology, environmental sciences and geochemistry. However, no available method has been reported. In this study, we provide a solution to visualize FeRM and evaluate their activity even at single cell level. Integrating with single cell sorter, FeRM can also be isolated from samples containing multispecies. This method can be used as a powerful tool to uncover the in-situ or ex-situ role of FeRM and their interactions with ambient microbes or chemicals.

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A targeted multi-omic analysis approach measures protein expression and low abundance transcripts on the single cell level

10.1101/700534 ◽

2019 ◽

Cited By ~ 1

Author(s):

Florian Mair ◽

Jami R. Erickson ◽

Valentin Voillet ◽

Yannick Simoni ◽

Timothy Bi ◽

...

Keyword(s):

Protein Expression ◽

Single Cell ◽

Cell Function ◽

Immune Cell ◽

High Sensitivity ◽

Read Depth ◽

Single Cell Level ◽

Cell Level ◽

Protein Datasets ◽

Relationship Of

SummaryHigh throughput single-cell RNA sequencing (sc-RNAseq) has become a frequently used tool to assess immune cell function and heterogeneity. Recently, the combined measurement of RNA and protein expression by sequencing was developed, which is commonly known as CITE-Seq. Acquisition of protein expression data along with transcriptome data resolves some of the limitations inherent to only assessing transcript, but also nearly doubles the sequencing read depth required per single cell. Furthermore, there is still a paucity of analysis tools to visualize combined transcript-protein datasets.Here, we describe a novel targeted transcriptomics approach that combines analysis of over 400 genes with simultaneous measurement of over 40 proteins on more than 25,000 cells. This targeted approach requires only about 1/10 of the read depth compared to a whole transcriptome approach while retaining high sensitivity for low abundance transcripts. To analyze these multi-omic transcript-protein datasets, we adapted One-SENSE for intuitive visualization of the relationship of proteins and transcripts on a single-cell level.

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