scholarly journals Single-cell analysis for drug development using convex lens-induced confinement imaging

BioTechniques ◽  
2019 ◽  
Vol 67 (5) ◽  
pp. 210-217 ◽  
Author(s):  
Ndeye Khady Thiombane ◽  
Nicolas Coutin ◽  
Daniel Berard ◽  
Radin Tahvildari ◽  
Sabrina Leslie ◽  
...  
Keyword(s):  
Single Cell ◽  
Drug Response ◽  
Drug Exposure ◽  
Cellular Proliferation ◽  
New Technologies ◽  
Detection Sensitivity ◽  
Yeast Cells ◽  
Sample Population ◽  
Phenotypic Analysis ◽  
Convex Lens

New technologies have powered rapid advances in cellular imaging, genomics and phenotypic analysis in life sciences. However, most of these methods operate at sample population levels and provide statistical averages of aggregated data that fail to capture single-cell heterogeneity, complicating drug discovery and development. Here we demonstrate a new single-cell approach based on convex lens-induced confinement (CLiC) microscopy. We validated CLiC on yeast cells, demonstrating subcellular localization with an enhanced signal-to-noise and fluorescent signal detection sensitivity compared with traditional imaging. In the live-cell CLiC assay, cellular proliferation times were consistent with flask culture. Using methotrexate, we provide drug response data showing a fivefold cell size increase following drug exposure. Taken together, CLiC enables high-quality imaging of single-cell drug response and proliferation for extended observation periods.

scholarly journals Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tracy M. Yamawaki ◽  
Daniel R. Lu ◽  
Daniel C. Ellwanger ◽  
Dev Bhatt ◽  
Paolo Manzanillo ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Cell Types ◽  
Data Interpretation ◽  
Detection Sensitivity ◽  
Rna Seq ◽  
Cell Recovery

Abstract Background Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

Development of a platform for single cell genomics using convex lens-induced confinement

Lab on a Chip ◽  
2015 ◽  
Vol 15 (14) ◽  
pp. 3013-3020 ◽  
Author(s):  
Sara Mahshid ◽  
Mohammed Jalal Ahamed ◽  
Daniel Berard ◽  
Susan Amin ◽  
Robert Sladek ◽  
...  
Keyword(s):  
Single Cell ◽  
Base Pair ◽  
Single Cell Analysis ◽  
Lab On A Chip ◽  
Cell Analysis ◽  
Next Generation ◽  
Single Cell Genomics ◽  
Convex Lens ◽  
Genomic Dnas

We present a lab-on-a-chip for the next generation of single-cell genomics, performing full-cycle single-cell analysis by demonstrating mega-base pair genomic DNAs in nanochannels extracted in situ.

scholarly journals Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Chayaporn Suphavilai ◽  
Shumei Chia ◽  
Ankur Sharma ◽  
Lorna Tu ◽  
Rafael Peres Da Silva ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Drug Response ◽  
Drug Repurposing ◽  
High Accuracy ◽  
Molecular Heterogeneity ◽  
Precision Oncology ◽  
Scale Analysis ◽  
Rna Seq ◽  
Large Scale Analysis

AbstractWhile understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc’s monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc.

scholarly journals Demographic and Socioeconomic Factors Influencing Uptake of Striga Weed Control Technologies in Kenya

2021 ◽  
pp. 51-59
Author(s):  
Godfrey Nakitare Nambafu ◽  
Richard Ndemo Onwonga
Keyword(s):  
Socioeconomic Factors ◽  
Weed Control ◽  
New Technologies ◽  
Control Strategies ◽  
Sampling Technique ◽  
Modern Technology ◽  
Sample Population ◽  
Population Type ◽  
Control Technologies ◽  
Land Size

Over the years, new technologies have been tested and introduced to control Striga in maize producing areas but adoption has remained low. The study done in 2013, determined the demographic and socioeconomic factors that influenced the adoption of Striga control technologies in Kisumu West, Bumula and Teso South sub counties of Western Kenya. Through Multi stage sampling technique, 40 households were selected per sub county for questionnaire administration; to gather information on demographic profiles of the sample population, type of fertilizer and seed variety used, income of the household, source of credit facilities and challenges faced in weed control. Chi square test at P<0.05 and logistic regression analysis, using R software was used to determine the relationship between demographic and socioeconomic factors and uptake of Striga control technologies. Farmers cited high cost, poor availability of improved varieties and lack of adequate knowledge as reasons for non-adoption of the Striga control strategies. Farmer’s age, education, land size and hiring of labour were found to significantly influence the adoption of the Striga control technologies. The low levels of adoption of modern technology indicate that they were not meeting farmers’ expectations, thus, researchers should put into consideration farmers’ education, age, land size and ability to high labour in their planning for an informed technology adoption. In addition, alternative options should be extended to farmers who are not able to use expensive technologies.

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 Extensive Regulation of Metabolism and Growth during the Cell Division Cycle

2014 ◽  
Author(s):  
Nikolai Slavov ◽  
David Botstein ◽  
Amy Caudy
Keyword(s):  
Gene Expression ◽  
Oxygen Consumption ◽  
Cell Division ◽  
Single Cell ◽  
Single Cells ◽  
Emergent Behavior ◽  
Yeast Cells ◽  
Physiological Data ◽  
Synchronized Cultures ◽  
Metabolic Cycle

Yeast cells grown in culture can spontaneously synchronize their respiration, metabolism, gene expression and cell division. Such metabolic oscillations in synchronized cultures reflect single-cell oscillations, but the relationship between the oscillations in single cells and synchronized cultures is poorly understood. To understand this relationship and the coordination between metabolism and cell division, we collected and analyzed DNA-content, gene-expression and physiological data, at hundreds of time-points, from cultures metabolically-synchronized at different growth rates, carbon sources and biomass densities. The data enabled us to extend and generalize our mechanistic model, based on ensemble average over phases (EAP), connecting the population-average gene-expression of asynchronous cultures to the gene-expression dynamics in the single-cells comprising the cultures. The extended model explains the carbon-source specific growth-rate responses of hundreds of genes. Our physiological data demonstrate that the frequency of metabolic cycling in synchronized cultures increases with the biomass density, suggesting that this cycling is an emergent behavior, resulting from the entraining of the single-cell metabolic cycle by a quorum-sensing mechanism, and thus underscoring the difference between metabolic cycling in single cells and in synchronized cultures. Measurements of constant levels of residual glucose across metabolically synchronized cultures indicate that storage carbohydrates are required to fuel not only the G1/S transition of the division cycle but also the metabolic cycle. Despite the large variation in profiled conditions and in the scale of their dynamics, most genes preserve invariant dynamics of coordination with each other and with the rate of oxygen consumption. Similarly, the G1/S transition always occurs at the beginning, middle or end of the high oxygen consumption phases, analogous to observations in human and drosophila cells. These results highlight evolutionary conserved coordination among metabolism, cell growth and division.

P-060: Subclone-specific microenvironmental impact and drug response in refractory multiple myeloma revealed by single cell transcriptomics

2021 ◽  
Vol 21 ◽  
pp. S71-S72
Author(s):  
Karsten Rippe ◽  
Stephan Tirier ◽  
Jan-Philipp Mallm ◽  
Simon Steiger ◽  
Alexandra Poos ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Single Cell ◽  
Drug Response ◽  
Refractory Multiple Myeloma

Addition of Drug-Response Specific Micro-RNAs to the International Prognostic Index Improves Prognostic Stratification of GCB-DLBCL Patients Treated with R-CHOP

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1623-1623 ◽  
Author(s):  
Karen Dybkær ◽  
Hanne Due ◽  
Rasmus Froberg Brøndum ◽  
Ken H. Young ◽  
Martin Bøgsted
Keyword(s):  
Disease Progression ◽  
Cell Line ◽  
Cell Lines ◽  
Mirna Expression ◽  
Drug Response ◽  
Drug Exposure ◽  
International Prognostic Index ◽  
Prognostic Index ◽  
Expression Data ◽  
Prognostic Stratification

Background: Patients with Diffuse large B-cell lymphoma (DLBCL) in approximately 40% of cases suffer from primary refractory disease and treatment induced immuno-chemotherapy resistance demonstrating that standard provided treatment regimens are not sufficient to cure all patients. Early detection of resistance is of great importance and defining microRNA (miRNA) involvement in resistance could be useful to guide treatment selection and help monitor treatment administration while sparing patients for inefficient, but still toxic therapy. Concept and Aims: With information on drug-response specific miRNAs, we hypothesized that multi-miRNA panels can improve robustness of individual clinical markers and serve as a prognostic classifier predicting disease progression in DLBCL patients. Methods: Fifteen DLBCL cell lines were tested for sensitivity towards rituximab (R), cyclophosphamide (C), doxorubicin (H), and vincristine (O). Cell line specific seeding concentrations was used to ensure exponential growth and each cell line was subjected to 16 concentrations in serial 2-fold dilutions and number of metabolic active cells was evaluated after 48 hours of drug exposure using MTS assay. For each drug, we ranked the cell lines according to their sensitivity and categorized them as sensitive, intermediate responsive, or resistant. Differential miRNA expression analysis between sensitive and resistant cell lines identified 43 miRNAs to be associated with response to compounds of the R-CHOP regimen, by selecting probes with a log fold change larger than 2. Baseline miRNA expression data were obtained for each cell line in untreated condition, and differential miRNA expression analysis identified 43 miRNAs associated to response to R-CHOP. Using the Affymetrix HG-U133+2 platform, expression levels of the miRNA precursors were assessed in 701 diagnostic DLBCL biopsies, and miRNA-panel classifiers were build using multiple Cox regression or random survival forest. Results: Generated prognostic miRNA-panel classifiers were tested for predictive accuracies and were subsequently evaluated by Brier scores and time varying area under the ROC curves (tAUC). Progression-free survival (PFS) was chosen as the outcome, since it is a treatment evaluation parameter as closely as possible to the time of drug exposure and the tested miRNAs were all associated directly to drug specific response. Furthermore, overall survival (OS) was used for verification of findings. Comparison of analyses conducted for the respective cohorts (All DLBCL, ABC, and GCB patients) showed the lowest prediction errors for all models within the GCB subclass with a multivariate Cox miRNA-panel model including miR-146a, miR-155, miR-21, miR-34a, and miR-23a~miR-27a~miR-24-2 cluster performed the best and successfully stratified GCB-DLBCL patients into high- and low-risk of disease progression. In addition, combination of the miRNA-panel and international prognostic index (IPI) substantially increased prognostic performance in GCB classified patients, indicating a prognostic signal from the response-specific miRNAs independent of IPI. In conclusion: We found as proof of concept that adding gene expression data detecting drug-response specific miRNAs to the clinically established IPI improved the prognostic stratification of GCB-DLBCL patients treated with R-CHOP. Disclosures No relevant conflicts of interest to declare.

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