Evaluating sources of technical variability in the mechano-node-pore sensing pipeline and their effect on the reproducibility of single-cell mechanical phenotyping
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ABSTRACT:
ORGANISM(S): Human cell lines
SUBMITTER: Brian Li
PROVIDER: S-BSST632 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:The mechanical properties of cells change with their differentiation, chronological age, and malignant progression. Consequently, these properties may be useful label-free biomarkers of various functional or clinically relevant cell states. Here, we demonstrate mechano-node-pore sensing (mechano-NPS), a multi-parametric single-cell-analysis method that utilizes a four-terminal measurement of the current across a microfluidic channel to quantify simultaneously cell diameter, resistance to compressive deformation, transverse deformation under constant strain, and recovery time after deformation. We define a new parameter, the whole-cell deformability index (wCDI), which provides a quantitative mechanical metric of the resistance to compressive deformation that can be used to discriminate among different cell types. The wCDI and the transverse deformation under constant strain show malignant MCF-7 and A549 cell lines are mechanically distinct from non-malignant, MCF-10A and BEAS-2B cell lines, and distinguishes between cells treated or untreated with cytoskeleton-perturbing small molecules. We categorize cell recovery time, ΔTr, as instantaneous (ΔTr ~ 0 ms), transient (ΔTr ≤ 40ms), or prolonged (ΔTr > 40ms), and show that the composition of recovery types, which is a consequence of changes in cytoskeletal organization, correlates with cellular transformation. Through the wCDI and cell-recovery time, mechano-NPS discriminates between sub-lineages of normal primary human mammary epithelial cells with accuracy comparable to flow cytometry, but without antibody labeling. Mechano-NPS identifies mechanical phenotypes that distinguishes lineage, chronological age, and stage of malignant progression in human epithelial cells.
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Project description:Task-fMRI researchers have great flexibility as to how they analyze their data, with multiple methodological options to choose from at each stage of the analysis workflow. While the development of tools and techniques has broadened our horizons for comprehending the complexities of the human brain, a growing body of research has highlighted the pitfalls of such methodological plurality. In a recent study, we found that the choice of software package used to run the analysis pipeline can have a considerable impact on the final group-level results of a task-fMRI investigation (Bowring et al., 2019, BMN). Here we revisit our work, seeking to identify the stages of the pipeline where the greatest variation between analysis software is induced. We carry out further analyses on the three datasets evaluated in BMN, employing a common processing strategy across parts of the analysis workflow and then utilizing procedures from three software packages (AFNI, FSL, and SPM) across the remaining steps of the pipeline. We use quantitative methods to compare the statistical maps and isolate the main stages of the workflow where the three packages diverge. Across all datasets, we find that variation between the packages' results is largely attributable to a handful of individual analysis stages, and that these sources of variability were heterogeneous across the datasets (e.g., choice of first-level signal model had the most impact for the balloon analog risk task dataset, while first-level noise model and group-level model were more influential for the false belief and antisaccade task datasets, respectively). We also observe areas of the analysis workflow where changing the software package causes minimal differences in the final results, finding that the group-level results were largely unaffected by which software package was used to model the low-frequency fMRI drifts.
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Project description:To design a robust quantitative proteomics study, an understanding of both the inherent heterogeneity of the biological samples being studied as well as the technical variability of the proteomics methods and platform is needed. Additionally, accurately identifying the technical steps associated with the largest variability would provide valuable information for the improvement and design of future processing pipelines. We present an experimental strategy that allows for a detailed examination of the variability of the quantitative LC-MS proteomics measurements. By replicating analyses at different stages of processing, various technical components can be estimated and their individual contribution to technical variability can be dissected. This design can be easily adapted to other quantitative proteomics pipelines. Herein, we applied this methodology to our label-free workflow for the processing of human brain tissue. For this application, the pipeline was divided into four critical components: Tissue dissection and homogenization (extraction), protein denaturation followed by trypsin digestion and SPE cleanup (digestion), short-term run-to-run instrumental response fluctuation (instrumental variance), and long-term drift of the quantitative response of the LC-MS/MS platform over the 2 week period of continuous analysis (instrumental stability). From this analysis, we found the following contributions to variability: extraction (72%) >> instrumental variance (16%) > instrumental stability (8.4%) > digestion (3.1%). Furthermore, the stability of the platform and its suitability for discovery proteomics studies is demonstrated.
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2014-01-15 | GSE54069 | GEO
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2009-02-20 | GSE12118 | GEO
Project description:Acute myocardial infarct (AMI) size depicted by late gadolinium enhancement cardiovascular magnetic resonance (CMR) is increasingly used as an efficacy endpoint in randomized trials comparing AMI therapies. Infarct size is quantified using manual planimetry (MANUAL), visual scoring (VISUAL), or automated techniques using signal-intensity thresholding (AUTO). Although AUTO is considered the most reproducible, prior studies did not account for the subjective determination of endocardial/epicardial borders, which all methods require. For MANUAL and VISUAL, prior studies did not address how to treat intermediate signal intensities due to partial volume.To assess sources of variability, AMI size was measured in 30 patients and 12 controls by 3 core-laboratories using 8 methods, each separated by more than 2 months time (n = 720 evaluations). The methods were: (1,2) AUTOSegment, AUTOFWHM (using Segment software or the full-width-at-half-maximum algorithm, respectively); (3,4) AUTO-UCSegment, AUTO-UCFWHM (user correction for endocardial border pixels, no-reflow, etc.); (5) MANUAL; (6) MANUAL-ISI (adjustment for intermediate signal-intensities); (7) VISUAL; (8) VISUAL-ISI.Mean infarct size varied between 16.8% and 27.2% of LV mass depending on method. Even automated techniques with no user interaction for infarct borders resulted in significant within-patient variability given the need to subjectively trace endocardial/epicardial contours. The coefficient-of-variation (CV) was 10.6% and 14.6% for AUTOSegment and AUTOFWHM, respectively. For manual and visual categories, reproducibility was improved when intermediate signal-intensities were considered (MANUAL-ISI vs MANUAL: CV = 8.3% vs 14.4%; p = 0.03; VISUAL-ISI vs VISUAL: CV = 8.4% vs 10.9%; p = 0.01). For AUTO-UCSegment, MANUAL-ISI, and VISUAL-ISI (best technique in each category) within-patient variability due to the quantification method was less than 10% of total variability, and the required sample sizes for detecting a 5% absolute difference in infarct size were 62, 63, and 62 patients, respectively.Among CMR core-laboratories, an important source of variability in infarct size quantification is the subjective delineation of endocardial/epicardial borders. When intermediate signal intensities are considered in manual planimetry and visual scoring, reproducibility and impact on sample size are similar to automated techniques.
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Project description:Background: RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript. Results: Independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage. Conclusions: Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.
2014-01-15 | GSE54068 | GEO
Project description:Background: RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript. Results: Independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage. Conclusions: Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.
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