scholarly journals Synchrotron macro ATR-FTIR microspectroscopy for high-resolution chemical mapping of single cells

The Analyst ◽  
2019 ◽  
Vol 144 (10) ◽  
pp. 3226-3238 ◽  
Author(s):  
Jitraporn Vongsvivut ◽  
David Pérez-Guaita ◽  
Bayden R. Wood ◽  
Philip Heraud ◽  
Karina Khambatta ◽  
...  
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
Spatial Resolution ◽  
Environmental Science ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Analysis ◽  
Chemical Mapping ◽  
Ftir Microspectroscopy ◽  
Resolution Single

Coupling synchrotron IR beam to an ATR element enhances spatial resolution suited for high-resolution single cell analysis in biology, medicine and environmental science.

scholarly journals μCB-seq: Microfluidic cell barcoding and sequencing for high-resolution imaging and sequencing of single cells

2020 ◽  
Author(s):  
Tyler N. Chen ◽  
Anushka Gupta ◽  
Mansi Zalavadia ◽  
Aaron M. Streets
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Protein Localization ◽  
Single Cells ◽  
Optical Measurements ◽  
Detection Sensitivity ◽  
High Resolution Imaging ◽  
Cell Analysis ◽  
Resolution Imaging

AbstractSingle-cell RNA sequencing (scRNA-seq) enables the investigation of complex biological processes in multicellular organisms with high resolution. However, many phenotypic features that are critical to understanding the functional role of cells in a heterogeneous tissue or organ are not directly encoded in the genome and therefore cannot be profiled with scRNA-seq. Quantitative optical microscopy has long been a powerful approach for characterizing diverse cellular phenotypes including cell morphology, protein localization, and chemical composition. Combining scRNA-seq with optical imaging has the potential to provide comprehensive single-cell analysis, allowing for functional integration of gene expression profiling and cell-state characterization. However, it is difficult to track single cells through both measurements; therefore, coupling current scRNA-seq protocols with optical measurements remains a challenge. Here, we report Microfluidic Cell Barcoding and Sequencing (μCB-seq), a microfluidic platform that combines high-resolution imaging and sequencing of single cells. μCB-seq is enabled by a novel fabrication method that preloads primers with known barcode sequences inside addressable reaction chambers of a microfluidic device. In addition to enabling multi-modal single-cell analysis, μCB-seq improves gene detection sensitivity, providing a scalable and accurate method for information-rich characterization of single cells.

scholarly journals Microfluidic platform accelerates tissue processing into single cells for molecular analysis and primary culture models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jeremy A. Lombardo ◽  
Marzieh Aliaghaei ◽  
Quy H. Nguyen ◽  
Kai Kessenbrock ◽  
Jered B. Haun
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Analysis ◽  
Processing Technologies ◽  
Microfluidic Platform ◽  
Tissue Processing ◽  
Single Cell Rna Sequencing ◽  
Culture Models ◽  
Tissue Digestion

AbstractTissues are complex mixtures of different cell subtypes, and this diversity is increasingly characterized using high-throughput single cell analysis methods. However, these efforts are hindered, as tissues must first be dissociated into single cell suspensions using methods that are often inefficient, labor-intensive, highly variable, and potentially biased towards certain cell subtypes. Here, we present a microfluidic platform consisting of three tissue processing technologies that combine tissue digestion, disaggregation, and filtration. The platform is evaluated using a diverse array of tissues. For kidney and mammary tumor, microfluidic processing produces 2.5-fold more single cells. Single cell RNA sequencing further reveals that endothelial cells, fibroblasts, and basal epithelium are enriched without affecting stress response. For liver and heart, processing time is dramatically reduced. We also demonstrate that recovery of cells from the system at periodic intervals during processing increases hepatocyte and cardiomyocyte numbers, as well as increases reproducibility from batch-to-batch for all tissues.

scholarly journals More than efficacy revealed by single-cell analysis of antiviral therapeutics

2019 ◽  
Author(s):  
Wu Liu ◽  
Mehmet U. Caglar ◽  
Zhangming Mao ◽  
Andrew Woodman ◽  
Jamie J. Arnold ◽  
...  
Keyword(s):  
Single Cell ◽  
Drug Targets ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Principal Component ◽  
Viral Population ◽  
Cell Analysis ◽  
Toxic Dose ◽  
Time Required

SUMMARYDevelopment of antiviral therapeutics emphasizes minimization of the effective dose and maximization of the toxic dose, first in cell culture and later in animal models. Long-term success of an antiviral therapeutic is determined not only by its efficacy but also by the duration of time required for drug-resistance to evolve. We have developed a microfluidic device comprised of ~6000 wells, with each well containing a microstructure to capture single cells. We have used this device to characterize enterovirus inhibitors with distinct mechanisms of action. In contrast to population methods, single-cell analysis reveals that each class of inhibitor interferes with the viral infection cycle in a manner that can be distinguished by principal component analysis. Single-cell analysis of antiviral candidates reveals not only efficacy but also properties of the members of the viral population most sensitive to the drug, the stage of the lifecycle most affected by the drug, and perhaps even if the drug targets an interaction of the virus with its host.

scholarly journals Methods and sensors for functional genomic studies of cell-cycle transitions in single cells

2020 ◽  
Vol 52 (10) ◽  
pp. 468-477
Author(s):  
Alexander C. Zambon ◽  
Tom Hsu ◽  
Seunghee Erin Kim ◽  
Miranda Klinck ◽  
Jennifer Stowe ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Imaging System ◽  
Single Cells ◽  
Cell Types ◽  
Cell Analysis ◽  
Mcf7 Cells ◽  
Genomic Studies ◽  
Cell Subpopulations

Much of our understanding of the regulatory mechanisms governing the cell cycle in mammals has relied heavily on methods that measure the aggregate state of a population of cells. While instrumental in shaping our current understanding of cell proliferation, these approaches mask the genetic signatures of rare subpopulations such as quiescent (G0) and very slowly dividing (SD) cells. Results described in this study and those of others using single-cell analysis reveal that even in clonally derived immortalized cancer cells, ∼1–5% of cells can exhibit G0 and SD phenotypes. Therefore to enable the study of these rare cell phenotypes we established an integrated molecular, computational, and imaging approach to track, isolate, and genetically perturb single cells as they proliferate. A genetically encoded cell-cycle reporter (K67p-FUCCI) was used to track single cells as they traversed the cell cycle. A set of R-scripts were written to quantify K67p-FUCCI over time. To enable the further study G0 and SD phenotypes, we retrofitted a live cell imaging system with a micromanipulator to enable single-cell targeting for functional validation studies. Single-cell analysis revealed HT1080 and MCF7 cells had a doubling time of ∼24 and ∼48 h, respectively, with high duration variability in G1 and G2 phases. Direct single-cell microinjection of mRNA encoding (GFP) achieves detectable GFP fluorescence within ∼5 h in both cell types. These findings coupled with the possibility of targeting several hundreds of single cells improves throughput and sensitivity over conventional methods to study rare cell subpopulations.

scholarly journals Live single cell analysis using synchrotron FTIR microspectroscopy: development of a simple dynamic flow system for prolonged sample viability

The Analyst ◽  
2019 ◽  
Vol 144 (3) ◽  
pp. 997-1007 ◽  
Author(s):  
James Doherty ◽  
Alan Raoof ◽  
Ansaf Hussain ◽  
Magda Wolna ◽  
Gianfelice Cinque ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Flow System ◽  
Cost Effective ◽  
Cell Analysis ◽  
Sample Holder ◽  
Liquid Sample ◽  
Dynamic Flow ◽  
Ftir Microspectroscopy ◽  
Live Cell Analysis

A simple, cost-effective liquid sample holder to perform single live cell analysis under an IR microscope.

scholarly journals Symposium on single cell analysis and genomic approaches, Experimental Biology 2017 Chicago, Illinois, April 23, 2017

2017 ◽  
Vol 49 (9) ◽  
pp. 491-495
Author(s):  
Hilary A. Coller
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Experimental Biology ◽  
Intratumor Heterogeneity ◽  
Cell Analysis ◽  
Single Cell Genomics ◽  
Critical Issues ◽  
Cell To Cell Variability ◽  
External Stimuli

Emerging technologies for the analysis of genome-wide information in single cells have the potential to transform many fields of biology, including our understanding of cell states, the response of cells to external stimuli, mosaicism, and intratumor heterogeneity. At Experimental Biology 2017 in Chicago, Physiological Genomics hosted a symposium in which five leaders in the field of single cell genomics presented their recent research. The speakers discussed emerging methodologies in single cell analysis and critical issues for the analysis of single cell data. Also discussed were applications of single cell genomics to understanding the different types of cells within an organism or tissue and the basis for cell-to-cell variability in response to stimuli.

scholarly journals Novel microfluidic platform accelerates processing of tissue into single cells for molecular analysis and primary culture models

2020 ◽  
Author(s):  
Jeremy Lombardo ◽  
Marzieh Aliaghaei ◽  
Quy Nguyen ◽  
Kai Kessenbrock ◽  
Jered Haun
Keyword(s):  
Single Cell ◽  
Stress Responses ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Types ◽  
Tissue Preparation ◽  
Cell Analysis ◽  
Processing Technologies ◽  
Microfluidic Platform ◽  
Wide Range

Abstract Tissues are composed of highly heterogeneous mixtures of cell subtypes, and this diversity is increasingly being characterized using high-throughput single cell analysis methods. However, these efforts are hindered by the fact that tissues must first be dissociated into single cell suspensions that are viable and still accurately represent phenotypes from the original tissue. Current methods for breaking down tissues are inefficient, labor-intensive, subject to high variability, and potentially biased towards cell subtypes that are easier to release. Here, we present a microfluidic platform consisting of three different tissue processing technologies that can perform the complete tissue to single cell workflow, including digestion, disaggregation, and filtration. First, we developed a new microfluidic digestion device that can be loaded with minced tissue specimens quickly and easily, and then use the combination of proteolytic enzyme activity and fluid shear forces to accelerate tissue breakdown. Next, we integrated dissociation and filter technologies into a single device, which enhanced single cell numbers and fully prepared the sample for single cell analysis. The final multi-device platform was then evaluated using a diverse array of tissue types that exhibited a wide range of properties. For murine kidney and mammary tumor, we found that microfluidic processing produced 2.5-fold more single, viable cells. Single cell RNA sequencing (scRNA-seq) further revealed that device processing enriched for endothelial cells, fibroblasts, and basal epithelium, and did not increase stress responses. For murine liver and heart, which are softer tissues containing fragile cell types, processing time could be reduced to 15 min, and even as short as 1 min. We also demonstrated that periodic recovery at defined time intervals produced substantially more hepatocytes and cardiomyocytes than continuous operation, most likely by preventing damage to fragile cell types. In future work, we will seek to integrate additional operations such as upstream tissue preparation and downstream microfluidic cell sorting and detection to create powerful point-of-care single cell diagnostic platforms.

scholarly journals Scattome: A Single-Cell Analysis of Targeted Transcriptome Program to Predict Drug Sensitivity of Single Cells within Human Myeloma Tumors

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4249-4249
Author(s):  
Amit Kumar Mitra ◽  
Ujjal Mukherjee ◽  
Taylor Harding ◽  
Holly Stessman ◽  
Ying Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Lines ◽  
Research Funding ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Pcr Analysis ◽  
Cell Analysis

Abstract Multiple myeloma (MM) is characterized by significant genetic diversity at subclonal levels that likely plays a defining role in the heterogeneity of tumor progression, clinical aggressiveness and drug sensitivity. Such heterogeneity is a driving factor in the evolution of MM, from founder clones through outgrowth of subclonal fractions. DNA Sequencing studies on MM samples have indeed demonstrated such heterogeneity in subclonal architecture at diagnosis based on recurrent mutations in pathologically relevant genes that may ultimately to lead to relapse. However, no study so far has reported a predictive gene expression signature that can identify, distinguish and quantify drug sensitive and drug-resistant subpopulations within a bulk population of myeloma cells. In recent years, our laboratory has successfully developed a gene expression profile (GEP)-based signature that could not only distinguish drug response of MM cell lines, but also was effective in stratifying patient outcomes when applied to GEP profiles from MM clinical trials using proteasome inhibitors (PI) as chemotherapeutic agents. Further, we noted myeloma cell lines that responded to the drug often contained residual sub-population of cells that did not respond, and likely were selectively propagated during drug treatment in vitro, and in patients. In this study, we performed targeted qRT-PCR analysis of single cells using a gene panel that included PI sensitivity genes and gene signatures that could discriminate between low and high-risk myeloma followed by intensive bioinformatics and statistical analysis for the classification and prediction of PI response in individual cells within bulk multiple myeloma tumors. Fluidigm's C1 Single-Cell Auto Prep System was used to perform automated single-cell capture, processing and cDNA synthesis on 576 pre-treatment cells from 12 cell lines representing a wide range of PI-sensitivity and 370 cells from 7 patient samples undergoing PI treatment followed by targeted gene expression profiling of single cells using automated, high-throughput on-chip qRT-PCR analysis using 96.96 Dynamic Array IFCs on the BioMark HD System. Probability of resistance for each individual cell was predicted using a pipeline that employed the machine learning methods Random Forest, Support Vector Machine (radial and sigmoidal), LASSO and kNN (k Nearest Neighbor) for making single-cell GEP data-driven predictions/ decisions. The weighted probabilities from each of the algorithms were used to quantify resistance of each individual cell and plotted using Ensemble forecasting algorithm. Using our drug response GEP signature at the single cell level, we could successfully identify distinct subpopulations of tumor cells that were predicted to be sensitive or resistant to PIs. Subsequently, we developed a R Statistical analysis package (http://cran.r-project.org), SCATTome (Single Cell Analysis of Targeted Transcriptome), that can restructure data obtained from Fluidigm qPCR analysis run, filter missing data, perform scaling of filtered data, build classification models and successfully predict drug response of individual cells and classify each cell's probability of response based on the targeted transcriptome. We will present the program output as graphical displays of single cell response probabilities. This package provides a novel classification method that has the potential to predict subclonal response to a variety of therapeutic agents. Disclosures Kumar: Skyline: Consultancy, Honoraria; BMS: Consultancy; Onyx: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Novartis: Research Funding; Takeda: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.

Micro/extended-nano sampling interface from a living single cell

The Analyst ◽  
2017 ◽  
Vol 142 (10) ◽  
pp. 1689-1696 ◽  
Author(s):  
L. Lin ◽  
K. Mawatari ◽  
K. Morikawa ◽  
Y. Pihosh ◽  
A. Yoshizaki ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Analysis ◽  
Sampling Interface ◽  
Living Single

Single-cell analysis is of increasing importance in many fields, but is challenging due to the ultra-small volumes (picoliters) of single cells.

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