scholarly journals Phenotypic Characterization of Antibiotic Persisters at the Single-Cell Level: From Data Acquisition to Data Analysis

2021 ◽  
pp. 95-106
Author(s):  
Nathan Fraikin ◽  
Laurence Van Melderen ◽  
Frédéric Goormaghtigh
Keyword(s):  
Data Analysis ◽  
Data Acquisition ◽  
Single Cell ◽  
Phenotypic Characterization ◽  
Single Cell Level ◽  
Cell Level

scholarly journals Characterization of the development of the mouse cochlear epithelium at the single cell level

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Likhitha Kolla ◽  
Michael C. Kelly ◽  
Zoe F. Mann ◽  
Alejandro Anaya-Rocha ◽  
Kathryn Ellis ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Level ◽  
Cell Level

scholarly journals A Mass-Ratiometry-Based CD45 Barcoding Method for Mass Cytometry Detection

2019 ◽  
Vol 24 (4) ◽  
pp. 408-419
Author(s):  
Hongu Meng ◽  
Antony Warden ◽  
Lulu Zhang ◽  
Ting Zhang ◽  
Yiyang Li ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Sample Size ◽  
Single Cell ◽  
Large Sample Size ◽  
Single Cell Level ◽  
Mass Cytometry ◽  
Cell Level ◽  
Large Sample ◽  
Antibody Staining ◽  
Instrument Sensitivity

Mass cytometry (CyTOF) is a critical cell profiling tool in acquiring multiparameter proteome data at the single-cell level. A major challenge in CyTOF analysis is sample-to-sample variance arising from the pipetting process, staining variation, and instrument sensitivity. To reduce such variations, cell barcoding strategies that enable the combination of individual samples prior to antibody staining and data acquisition on CyTOF are often utilized. The most prevalent barcoding strategy is based on a binary scheme that cross-examines the existence or nonexistence of certain mass signals; however, it is limited by low barcoding efficiency and high cost, especially for large sample size. Herein, we present a novel barcoding method for CyTOF application based on mass ratiometry. Different mass tags with specific fixed ratios are used to label CD45 antibody to achieve sample barcoding. The presented method exponentially increases the number of possible barcoded samples with the same amount of mass tags compared with conventional methods. It also reduces the overall time for the labeling process to 40 min and avoids the need for expensive commercial barcoding buffer reagents. Moreover, unlike the conventional barcoding process, this strategy does not pre-permeabilize cells before the barcoding procedure, which offers additional benefits in preserving surface biomarker signals.

scholarly journals Cross-Tissue Characterization of Heterogeneities of Mesenchymal Stem Cells and Their Differentiation Potentials

2021 ◽  
Vol 9 ◽  
Author(s):  
Wenhong Hou ◽  
Li Duan ◽  
Changyuan Huang ◽  
Xingfu Li ◽  
Xiao Xu ◽  
...  
Keyword(s):  
Single Cell ◽  
Umbilical Cord ◽  
Stromal Cells ◽  
Tissue Characterization ◽  
Single Cell Level ◽  
Rna Seq ◽  
Cell Level ◽  
Cell Sources ◽  
Cell Subpopulations

Mesenchymal stem/stromal cells (MSCs) are promising cell sources for regenerative medicine and the treatment of autoimmune disorders. Comparing MSCs from different tissues at the single-cell level is fundamental for optimizing clinical applications. Here we analyzed single-cell RNA-seq data of MSCs from four tissues, namely umbilical cord, bone marrow, synovial tissue, and adipose tissue. We identified three major cell subpopulations, namely osteo-MSCs, chondro-MSCs, and adipo/myo-MSCs, across all MSC samples. MSCs from the umbilical cord exhibited the highest immunosuppression, potentially indicating it is the best immune modulator for autoimmune diseases. MSC subpopulations, with different subtypes and tissue sources, showed pronounced differences in differentiation potentials. After we compared the cell subpopulations and cell status pre-and-post chondrogenesis induction, osteogenesis induction, and adipogenesis induction, respectively, we found MSC subpopulations expanded and differentiated when their subtypes consist with induction directions, while the other subpopulations shrank. We identified the genes and transcription factors underlying each induction at the single-cell level and subpopulation level, providing better targets for improving induction efficiency.

scholarly journals Stitching and registering highly multiplexed whole slide images of tissues and tumors using ASHLAR software

2021 ◽  
Author(s):  
Jeremy Muhlich ◽  
Yu-An Chen ◽  
Douglas Russell ◽  
Peter K Sorger
Keyword(s):  
Spatial Analysis ◽  
High Resolution ◽  
Data Acquisition ◽  
Single Cell ◽  
Computational Analysis ◽  
Single Cell Level ◽  
Whole Slide Imaging ◽  
Cell Level ◽  
Successive Image ◽  
Whole Slide Images

ABSTRACTWidespread use of highly multiplexed microscopy to study normal and diseased tissues at a single-cell level is complicated by underdevelopment of the necessary software. This is particularly true of high resolution whole-slide imaging (WSI), which involves gigapixel datasets of specimens as large as 5 cm2. WSI is necessary for accurate spatial analysis and a diagnostic necessity. High resolution WSI requires collection of successive image tiles; multiplexing commonly involves successive data acquisition cycles, each with a subset of dyes, antibodies or oligonucleotides. We describe a new Python tool, ASHLAR (Alignment by Simultaneous Harmonization of Layer/Adjacency Registration), that coordinates stitching and registration and scales to 103 or more image tiles over many imaging cycles to generate accurate, high-plex image mosaics, the key type of data for downstream visualization and computational analysis. ASHLAR is more robust and accurate than existing methods and compatible with any scanner or microscope conforming to Open Microscopy Environment standards.

scholarly journals Application of Imaging Flow Cytometry for the Characterization of Intracellular Attributes in Chinese Hamster Ovary Cell Lines at the Single‐Cell Level

2019 ◽  
Vol 14 (7) ◽  
pp. 1800675 ◽  
Author(s):  
Eva Pekle ◽  
Andrew Smith ◽  
Guglielmo Rosignoli ◽  
Christopher Sellick ◽  
C. M. Smales ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
Cell Lines ◽  
Chinese Hamster Ovary ◽  
Chinese Hamster ◽  
Ovary Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
Imaging Flow Cytometry

Surface-enhanced Raman spectroscopy of microorganisms: limitations and applicability on the single-cell level

The Analyst ◽  
2019 ◽  
Vol 144 (3) ◽  
pp. 943-953 ◽  
Author(s):  
Ruben Weiss ◽  
Márton Palatinszky ◽  
Michael Wagner ◽  
Reinhard Niessner ◽  
Martin Elsner ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Single Cell ◽  
Environmental Studies ◽  
Surface Enhanced Raman Spectroscopy ◽  
Clinical Diagnostics ◽  
Single Cell Level ◽  
Cell Level ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Detection and characterization of microorganisms is essential for both clinical diagnostics and environmental studies.

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 Identifying tumor cells at the single cell level

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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