A microsystem integrating photodegradable hydrogel microstructures and reconfigurable microfluidics for single-cell analysis and retrieval

We developed a micropatterned photodegradable hydrogel array integrated with reconfigurable microfluidics to enable cell secretion analysis and cell retrieval at the single-cell level.

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Single-Cell Transcriptome Analysis as a Promising Tool to Study Pluripotent Stem Cell Reprogramming

International Journal of Molecular Sciences ◽

10.3390/ijms22115988 ◽

2021 ◽

Vol 22 (11) ◽

pp. 5988

Author(s):

Hyun Kyu Kim ◽

Tae Won Ha ◽

Man Ryul Lee

Keyword(s):

Stem Cells ◽

Single Cell ◽

Cell Fate ◽

Human Cell ◽

Transcriptome Analysis ◽

Single Cell Analysis ◽

Embryonic Stem ◽

Single Cell Level ◽

Cell Analysis ◽

Cell Level

Cells are the basic units of all organisms and are involved in all vital activities, such as proliferation, differentiation, senescence, and apoptosis. A human body consists of more than 30 trillion cells generated through repeated division and differentiation from a single-cell fertilized egg in a highly organized programmatic fashion. Since the recent formation of the Human Cell Atlas consortium, establishing the Human Cell Atlas at the single-cell level has been an ongoing activity with the goal of understanding the mechanisms underlying diseases and vital cellular activities at the level of the single cell. In particular, transcriptome analysis of embryonic stem cells at the single-cell level is of great importance, as these cells are responsible for determining cell fate. Here, we review single-cell analysis techniques that have been actively used in recent years, introduce the single-cell analysis studies currently in progress in pluripotent stem cells and reprogramming, and forecast future studies.

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Microfluidic Single-Cell Manipulation and Analysis: Methods and Applications

Micromachines ◽

10.3390/mi10020104 ◽

2019 ◽

Vol 10 (2) ◽

pp. 104 ◽

Cited By ~ 38

Author(s):

Tao Luo ◽

Lei Fan ◽

Rong Zhu ◽

Dong Sun

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Single Cells ◽

Petri Dish ◽

Cell Manipulation ◽

Single Cell Level ◽

Cell Analysis ◽

Cell Level ◽

Advantages And Disadvantages ◽

Single Cell Manipulation

In a forest of a hundred thousand trees, no two leaves are alike. Similarly, no two cells in a genetically identical group are the same. This heterogeneity at the single-cell level has been recognized to be vital for the correct interpretation of diagnostic and therapeutic results of diseases, but has been masked for a long time by studying average responses from a population. To comprehensively understand cell heterogeneity, diverse manipulation and comprehensive analysis of cells at the single-cell level are demanded. However, using traditional biological tools, such as petri-dishes and well-plates, is technically challengeable for manipulating and analyzing single-cells with small size and low concentration of target biomolecules. With the development of microfluidics, which is a technology of manipulating and controlling fluids in the range of micro- to pico-liters in networks of channels with dimensions from tens to hundreds of microns, single-cell study has been blooming for almost two decades. Comparing to conventional petri-dish or well-plate experiments, microfluidic single-cell analysis offers advantages of higher throughput, smaller sample volume, automatic sample processing, and lower contamination risk, etc., which made microfluidics an ideal technology for conducting statically meaningful single-cell research. In this review, we will summarize the advances of microfluidics for single-cell manipulation and analysis from the aspects of methods and applications. First, various methods, such as hydrodynamic and electrical approaches, for microfluidic single-cell manipulation will be summarized. Second, single-cell analysis ranging from cellular to genetic level by using microfluidic technology is summarized. Last, we will also discuss the advantages and disadvantages of various microfluidic methods for single-cell manipulation, and then outlook the trend of microfluidic single-cell analysis.

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Quantitative PCR Analysis of DNA, RNAs, and Proteins in the Same Single Cell

Clinical Chemistry ◽

10.1373/clinchem.2012.191445 ◽

2012 ◽

Vol 58 (12) ◽

pp. 1682-1691 ◽

Cited By ~ 44

Author(s):

Anders Ståhlberg ◽

Christer Thomsen ◽

David Ruff ◽

Pierre Åman

Keyword(s):

Single Cell ◽

Quantitative Pcr ◽

Single Cell Analysis ◽

Basic Unit ◽

Proximity Ligation Assay ◽

Single Cell Level ◽

Cell Analysis ◽

Cell Level ◽

Correlation Studies ◽

Intracellular Concentrations

BACKGROUND The single cell represents the basic unit of all organisms. Most investigations have been performed on large cell populations, but understanding cell dynamics and heterogeneity requires single-cell analysis. Current methods for single-cell analysis generally can detect only one class of analytes. METHODS Reverse transcription and the proximity ligation assay were coupled with quantitative PCR and used to quantify any combination of DNA, mRNAs, microRNAs (miRNAs), noncoding RNAs (ncRNAs), and proteins from the same single cell. The method was used on transiently transfected human cells to determine the intracellular concentrations of plasmids, their transcribed mRNAs, translated proteins, and downstream RNA targets. RESULTS We developed a whole-cell lysis buffer to release unfractionated DNA, RNA, and proteins that would not degrade any detectable analyte or inhibit the assay. The dynamic range, analytical sensitivity, and specificity for quantifying DNA, mRNAs, miRNAs, ncRNAs, and proteins were shown to be accurate down to the single-cell level. Correlation studies revealed that the intracellular concentrations of plasmids and their transcribed mRNAs were correlated only moderately with translated protein concentrations (Spearman correlation coefficient, 0.37 and 0.31, respectively; P < 0.01). In addition, an ectopically expressed gene affected the correlations between analytes and this gene, which is related to gene regulation. CONCLUSIONS This method is compatible with most cell-sampling approaches, and generates output for the same parameter for all measured analytes, a feature facilitating comparative data analysis. This approach should open up new avenues in molecular diagnostics for detailed correlation studies of multiple and different classes of analytes at the single-cell level.

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Co-detection of ALDH1A1, ABCG2, ALCAM and CD133 in three A549 subpopulations at the single cell level by one-step digital RT-PCR

Integrative Biology ◽

10.1039/c8ib00042e ◽

2018 ◽

Vol 10 (6) ◽

pp. 364-369 ◽

Cited By ~ 2

Author(s):

Yanan Xu ◽

Jiumei Hu ◽

Qiangyuan Zhu ◽

Qi Song ◽

Ying Mu

Keyword(s):

Stem Cell ◽

Cancer Stem Cell ◽

Single Cell ◽

Single Cell Level ◽

Cell Analysis ◽

Rt Pcr ◽

Cell Level ◽

One Step ◽

Single Cancer ◽

Co Detection

Single cancer stem cell analysis of four biomarker genes by microfluidic one-step digital RT-PCR.

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Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

10.21203/rs.3.rs-977693/v2 ◽

2021 ◽

Author(s):

Yuefan Wang ◽

Tung-Shing Mamie Lih ◽

Lijun Chen ◽

Yuanwei Xu ◽

Morgan D. Kuczler ◽

...

Keyword(s):

Single Cell ◽

Proteomic Analysis ◽

Single Cell Analysis ◽

Cancer Cell Line ◽

Cellular Heterogeneity ◽

Single Cell Level ◽

Cell Level ◽

Data Independent Acquisition ◽

Peptide Precursors ◽

Proteomic Study

Abstract Background: Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Methods: We report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. Results: We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Conclusions: Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

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Single cell infection with influenza A virus using drop-based microfluidics

10.1101/2021.09.14.460333 ◽

2021 ◽

Author(s):

Emma Kate Loveday ◽

Humberto S. Sanchez ◽

Mallory M. Thomas ◽

Connie B. Chang

Keyword(s):

Single Cell ◽

Influenza A Virus ◽

Influenza A ◽

Single Cell Analysis ◽

Rna Virus ◽

Host Cells ◽

Single Cell Level ◽

Cell Infection ◽

Cell Level ◽

High Genetic Diversity

SummaryInfluenza A virus (IAV) is an RNA virus with high genetic diversity which necessitates the development of new vaccines targeting emerging mutations each year. As IAV exists in genetically heterogeneous populations, current studies focus on understanding population dynamics at the single cell level. These studies include novel methodology that can be used for probing populations at the single cell level, such as single cell sequencing and microfluidics. Here, we introduce a drop-based microfluidics method to study IAV infection at a single cell level by isolating infected host cells in microscale drops. Single human alveolar basal epithelial (A549), Madin-Darby Canine Kidney cells (MDCK) and MDCK + human siat7e gene (Siat7e) cells infected with the pandemic A/California/07/2009 (H1N1) strain were encapsulated within 50 μm radii drops and incubated at 37°C. We demonstrate that drops remain stable over 24 hours, that 75% of cells remain viable, and that IAV virus can propagate within the drops. Drop-based microfluidics therefore enables single cell analysis of viral populations produced from individually infected cells.

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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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Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

10.1101/2021.08.19.457000 ◽

2021 ◽

Author(s):

Yuefan Wang ◽

Tung-Shing Mamie Lih ◽

Lijun Chen ◽

Yuanwei Xu ◽

Morgan D Kuczler ◽

...

Keyword(s):

Single Cell ◽

Proteomic Analysis ◽

Single Cell Analysis ◽

Cancer Cell Line ◽

Cellular Heterogeneity ◽

Single Cell Level ◽

Cell Level ◽

Data Independent Acquisition ◽

Peptide Precursors ◽

Proteomic Study

Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Herein, we report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Our results demonstrates that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

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Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

10.21203/rs.3.rs-977693/v1 ◽

2021 ◽

Author(s):

Yuefan Wang ◽

T. Mamie Lih ◽

Lijun Chen ◽

Yuanwei Xu ◽

Morgan Kuczler ◽

...

Keyword(s):

Single Cell ◽

Proteomic Analysis ◽

Single Cell Analysis ◽

Cancer Cell Line ◽

Cellular Heterogeneity ◽

Single Cell Level ◽

Cell Level ◽

Data Independent Acquisition ◽

Peptide Precursors ◽

Proteomic Study

Abstract Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Herein, we report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

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Challenges and emerging directions in single-cell analysis

10.1101/127761 ◽

2017 ◽

Author(s):

Guo-Cheng Yuan ◽

Long Cai ◽

Michael Elowitz ◽

Tariq Enver ◽

Guoping Fan ◽

...

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Systematic Investigation ◽

Cellular Heterogeneity ◽

Cell Analysis ◽

Cell Level ◽

Wide Range ◽

Molecular Information ◽

The Individual ◽

Genome Scale

AbstractSingle-cell analysis is a rapidly evolving approach to characterize genome-scale molecular information at the individual cell level. Development of single-cell technologies and computational methods has enabled systematic investigation of cellular heterogeneity in a wide range of tissues and cell populations, yielding fresh insights into the composition, dynamics, and regulatory mechanisms of cell states in development and disease. Despite substantial advances, significant challenges remain in the analysis, integration, and interpretation of single-cell omics data. Here, we discuss the state of the field and recent advances, and look to future opportunities.

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